بسم الله الرحمن الرحيم

Team Members

• Omar Morshdy
• Dima Ayad
• Mohamed Ashraf
• Philopater Ayman
• Omar Reda

Project Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Competition's link: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

In this Notebook i will go through:

Remember: Any information that i state about a feature is present in data_descripition.txt

---

Import All libraries we may need

import numpy as np
import pandas as pd
pd.set_option('display.max_columns',None) # 3shan 22dr ashouf koul al colums
pd.set_option('display.max_row',100) # 3shan 22dr ashouf koul al rows
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline

# https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import KFold, cross_val_score

import scipy.stats
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

# almktba de gamda w hnshoof ht3ml eh 2odam hwa mn asm al methods algybnha mn gwaha byna alhya compare models
from pycaret.regression import setup, compare_models, predict_model 

from catboost import CatBoostRegressor
import plotly.graph_objects as go
import plotly.express as px
import scipy.stats as stats
from IPython.display import display, HTML
from sklearn.linear_model import LinearRegression

Read Data Frame

Data is splited to three

1. trained data
2. testing data
3. sample submission to know your accuracy in the end

train0 = pd.read_csv('train.csv')
test0 = pd.read_csv('test.csv')
sample_submission = pd.read_csv('sample_submission.csv')

Data shape

79 feature explanatory variables

1460 train data + 1459 test data will give 2919 rows

print("Train_data shape = ",train0.shape)
print("Test_data shape = ",test0.shape)

Train_data shape =  (1460, 81)
Test_data shape =  (1459, 80)

train0.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	0	150	856	GasA	Ex	Y	SBrkr	856	854	0	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	0	284	1262	GasA	Ex	Y	SBrkr	1262	0	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	0	434	920	GasA	Ex	Y	SBrkr	920	866	0	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	0	540	756	GasA	Gd	Y	SBrkr	961	756	0	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	0	490	1145	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

# test -> is the same train data but not have SalePrice da altby3y y3ny (●'◡'●)
test0.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	0	896	0.0	0.0	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	0	0	120	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	0	1329	0.0	0.0	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	0	0	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	0	1629	0.0	0.0	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	0	0	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	0	1604	0.0	0.0	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	0	0	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	0	1280	0.0	0.0	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	0	0	144	0	NaN	NaN	NaN	0	1	2010	WD	Normal

sample_submission

	Id	SalePrice
0	1461	169277.052498
1	1462	187758.393989
2	1463	183583.683570
3	1464	179317.477511
4	1465	150730.079977
...	...	...
1454	2915	167081.220949
1455	2916	164788.778231
1456	2917	219222.423400
1457	2918	184924.279659
1458	2919	187741.866657

1459 rows × 2 columns

81 feature in our dataset, 1 is our target variable, 1 is ID column which we can ignore, leaving us with 79 explanatory variables

target = train0['SalePrice']
train1 = train0.drop(['Id','SalePrice'], axis=1) # hndrop mn hna al target bt3na whwa al SalePrice and Id 3shan lma negy n3ml concat m3 al test 2odam wkman doul msh 3yznhom dlw2ty
test_ids = test0['Id']
test1 = test0.drop('Id', axis=1)

train1.head()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	0	150	856	GasA	Ex	Y	SBrkr	856	854	0	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal
1	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	0	284	1262	GasA	Ex	Y	SBrkr	1262	0	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal
2	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	0	434	920	GasA	Ex	Y	SBrkr	920	866	0	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal
3	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	0	540	756	GasA	Gd	Y	SBrkr	961	756	0	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml
4	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	0	490	1145	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal

test1.head()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	0	896	0.0	0.0	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	0	0	120	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	0	1329	0.0	0.0	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	0	0	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	0	1629	0.0	0.0	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	0	0	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	0	1604	0.0	0.0	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	0	0	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	0	1280	0.0	0.0	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	0	0	144	0	NaN	NaN	NaN	0	1	2010	WD	Normal

That's a lot of columns. In simple words, we need to be selective of what features to include. Note: Read the data description carefully to understand the column features.

---

Explore Numerical Features

def summary_num(numerical_features):
        """ Function to summarize all numeric features"""
        print(f'There are {len(numerical_features)} observation in our dataset, {len(numerical_features[numerical_features.duplicated()])} of which are duplicated')
        desc = numerical_features.describe().T
        # hnzwd dlw2ty kam haga mn 3ndna
        desc['nunique'] = numerical_features.nunique() # hya number of unique value tknow is it Continous feature or categoricalfeatures
        desc['%unique'] = numerical_features.nunique() / len(numerical_features) * 100
        desc['Null'] = numerical_features.isna().sum()
        return desc

numerical_features = train1.select_dtypes(include=[np.number])
summary_num(numerical_features=numerical_features)

There are 1460 observation in our dataset, 1 of which are duplicated

	count	mean	std	min	25%	50%	75%	max	nunique	%unique	Null
MSSubClass	1460.0	56.897260	42.300571	20.0	20.00	50.0	70.00	190.0	15	1.027397	0
LotFrontage	1201.0	70.049958	24.284752	21.0	59.00	69.0	80.00	313.0	110	7.534247	259
LotArea	1460.0	10516.828082	9981.264932	1300.0	7553.50	9478.5	11601.50	215245.0	1073	73.493151	0
OverallQual	1460.0	6.099315	1.382997	1.0	5.00	6.0	7.00	10.0	10	0.684932	0
OverallCond	1460.0	5.575342	1.112799	1.0	5.00	5.0	6.00	9.0	9	0.616438	0
YearBuilt	1460.0	1971.267808	30.202904	1872.0	1954.00	1973.0	2000.00	2010.0	112	7.671233	0
YearRemodAdd	1460.0	1984.865753	20.645407	1950.0	1967.00	1994.0	2004.00	2010.0	61	4.178082	0
MasVnrArea	1452.0	103.685262	181.066207	0.0	0.00	0.0	166.00	1600.0	327	22.397260	8
BsmtFinSF1	1460.0	443.639726	456.098091	0.0	0.00	383.5	712.25	5644.0	637	43.630137	0
BsmtFinSF2	1460.0	46.549315	161.319273	0.0	0.00	0.0	0.00	1474.0	144	9.863014	0
BsmtUnfSF	1460.0	567.240411	441.866955	0.0	223.00	477.5	808.00	2336.0	780	53.424658	0
TotalBsmtSF	1460.0	1057.429452	438.705324	0.0	795.75	991.5	1298.25	6110.0	721	49.383562	0
1stFlrSF	1460.0	1162.626712	386.587738	334.0	882.00	1087.0	1391.25	4692.0	753	51.575342	0
2ndFlrSF	1460.0	346.992466	436.528436	0.0	0.00	0.0	728.00	2065.0	417	28.561644	0
LowQualFinSF	1460.0	5.844521	48.623081	0.0	0.00	0.0	0.00	572.0	24	1.643836	0
GrLivArea	1460.0	1515.463699	525.480383	334.0	1129.50	1464.0	1776.75	5642.0	861	58.972603	0
BsmtFullBath	1460.0	0.425342	0.518911	0.0	0.00	0.0	1.00	3.0	4	0.273973	0
BsmtHalfBath	1460.0	0.057534	0.238753	0.0	0.00	0.0	0.00	2.0	3	0.205479	0
FullBath	1460.0	1.565068	0.550916	0.0	1.00	2.0	2.00	3.0	4	0.273973	0
HalfBath	1460.0	0.382877	0.502885	0.0	0.00	0.0	1.00	2.0	3	0.205479	0
BedroomAbvGr	1460.0	2.866438	0.815778	0.0	2.00	3.0	3.00	8.0	8	0.547945	0
KitchenAbvGr	1460.0	1.046575	0.220338	0.0	1.00	1.0	1.00	3.0	4	0.273973	0
TotRmsAbvGrd	1460.0	6.517808	1.625393	2.0	5.00	6.0	7.00	14.0	12	0.821918	0
Fireplaces	1460.0	0.613014	0.644666	0.0	0.00	1.0	1.00	3.0	4	0.273973	0
GarageYrBlt	1379.0	1978.506164	24.689725	1900.0	1961.00	1980.0	2002.00	2010.0	97	6.643836	81
GarageCars	1460.0	1.767123	0.747315	0.0	1.00	2.0	2.00	4.0	5	0.342466	0
GarageArea	1460.0	472.980137	213.804841	0.0	334.50	480.0	576.00	1418.0	441	30.205479	0
WoodDeckSF	1460.0	94.244521	125.338794	0.0	0.00	0.0	168.00	857.0	274	18.767123	0
OpenPorchSF	1460.0	46.660274	66.256028	0.0	0.00	25.0	68.00	547.0	202	13.835616	0
EnclosedPorch	1460.0	21.954110	61.119149	0.0	0.00	0.0	0.00	552.0	120	8.219178	0
3SsnPorch	1460.0	3.409589	29.317331	0.0	0.00	0.0	0.00	508.0	20	1.369863	0
ScreenPorch	1460.0	15.060959	55.757415	0.0	0.00	0.0	0.00	480.0	76	5.205479	0
PoolArea	1460.0	2.758904	40.177307	0.0	0.00	0.0	0.00	738.0	8	0.547945	0
MiscVal	1460.0	43.489041	496.123024	0.0	0.00	0.0	0.00	15500.0	21	1.438356	0
MoSold	1460.0	6.321918	2.703626	1.0	5.00	6.0	8.00	12.0	12	0.821918	0
YrSold	1460.0	2007.815753	1.328095	2006.0	2007.00	2008.0	2009.00	2010.0	5	0.342466	0

Insights from numerical features summary:

• There are many numerical variables that are categorical and need to be treated as such during feature engineering
• Most of features doesn't have null values ( Those which have must be checked)
• No duplicate values

---

Explore Categorical Features

def summary_cat(categorical_features):
    """ Function to summarize all Categorical features"""

    print(f'There are {len(categorical_features)} observation in our dataset, {len(categorical_features[categorical_features.duplicated()])} of which are duplicated')
    
    desc = categorical_features.describe().T
    unique = categorical_features.nunique()
    desc['%unique'] = unique / len(categorical_features) * 100
    desc['Null'] = categorical_features.isna().sum()
    return desc

categorical_features = train1.select_dtypes(include=['object'])
summary_cat(categorical_features=categorical_features)

There are 1460 observation in our dataset, 21 of which are duplicated

	count	unique	top	freq	%unique	Null
MSZoning	1460	5	RL	1151	0.342466	0
Street	1460	2	Pave	1454	0.136986	0
Alley	91	2	Grvl	50	0.136986	1369
LotShape	1460	4	Reg	925	0.273973	0
LandContour	1460	4	Lvl	1311	0.273973	0
Utilities	1460	2	AllPub	1459	0.136986	0
LotConfig	1460	5	Inside	1052	0.342466	0
LandSlope	1460	3	Gtl	1382	0.205479	0
Neighborhood	1460	25	NAmes	225	1.712329	0
Condition1	1460	9	Norm	1260	0.616438	0
Condition2	1460	8	Norm	1445	0.547945	0
BldgType	1460	5	1Fam	1220	0.342466	0
HouseStyle	1460	8	1Story	726	0.547945	0
RoofStyle	1460	6	Gable	1141	0.410959	0
RoofMatl	1460	8	CompShg	1434	0.547945	0
Exterior1st	1460	15	VinylSd	515	1.027397	0
Exterior2nd	1460	16	VinylSd	504	1.095890	0
MasVnrType	1452	4	None	864	0.273973	8
ExterQual	1460	4	TA	906	0.273973	0
ExterCond	1460	5	TA	1282	0.342466	0
Foundation	1460	6	PConc	647	0.410959	0
BsmtQual	1423	4	TA	649	0.273973	37
BsmtCond	1423	4	TA	1311	0.273973	37
BsmtExposure	1422	4	No	953	0.273973	38
BsmtFinType1	1423	6	Unf	430	0.410959	37
BsmtFinType2	1422	6	Unf	1256	0.410959	38
Heating	1460	6	GasA	1428	0.410959	0
HeatingQC	1460	5	Ex	741	0.342466	0
CentralAir	1460	2	Y	1365	0.136986	0
Electrical	1459	5	SBrkr	1334	0.342466	1
KitchenQual	1460	4	TA	735	0.273973	0
Functional	1460	7	Typ	1360	0.479452	0
FireplaceQu	770	5	Gd	380	0.342466	690
GarageType	1379	6	Attchd	870	0.410959	81
GarageFinish	1379	3	Unf	605	0.205479	81
GarageQual	1379	5	TA	1311	0.342466	81
GarageCond	1379	5	TA	1326	0.342466	81
PavedDrive	1460	3	Y	1340	0.205479	0
PoolQC	7	3	Gd	3	0.205479	1453
Fence	281	4	MnPrv	157	0.273973	1179
MiscFeature	54	4	Shed	49	0.273973	1406
SaleType	1460	9	WD	1267	0.616438	0
SaleCondition	1460	6	Normal	1198	0.410959	0

Insights from Categorical features summary:

• There are many null values in multiple features (Must be checked) (Null values might represent feature not present in house??)
• No duplicate values

---

Let's Explore Our Data

Let's take a look on the target variable

target.describe()

count      1460.000000
mean     180921.195890
std       79442.502883
min       34900.000000
25%      129975.000000
50%      163000.000000
75%      214000.000000
max      755000.000000
Name: SalePrice, dtype: float64

plt.plot(figsize=(10,10), dpi=300)
sns.histplot(x=target, bins=30, kde=True)
plt.show()

# skewness
print("Skewness: %f" % target.skew())

Skewness: 1.882876

• Nothing seems weird about sales prices distribution as it's normal for most houses to be priced at a median, with a small amount of outliers priced expensively
• It deviate from normal distribution
• Positive skew

---

Let's take a look on Numerical data for following purposes:

• Check relationship between feature and target variable
• Check out Distribution
• Check if that data is categorical but saved as numeric
• Understand each feature for purpose of feature engineering
  • i want to go through this slow approach of plotting each feature to clearly understand it and figure out new features

# we have 36 numeric feature
numerical_features.shape

(1460, 36)

numerical_features.columns

Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',
       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',
       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',
       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
       'MoSold', 'YrSold'],
      dtype='object')

# tb3n a7na 3rfeen wshofna mn al description al foo2 an 3ndna fe hgat category gwa bs numeric f3yzeen n3rf hya eh wn7otha fe list
cat_feat = []

numerical_features.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);

MSSubClass feature: Identifies the type of dwelling involved in the sale.

• from it's histogram distribution it seems that it's a categorical variable

numerical_features['MSSubClass'].unique()

array([ 60,  20,  70,  50, 190,  45,  90, 120,  30,  85,  80, 160,  75,
       180,  40], dtype=int64)

• Each unique number idenity type of dweling
• This feature should be changed into categorical in feature engineering

cat_feat.append('MSSubClass')

sns.boxplot( x=numerical_features['MSSubClass'], y=target)

<AxesSubplot:xlabel='MSSubClass', ylabel='SalePrice'>

• It seems that different MSSubClass have different prices, and that some types have alot of outliers

LotFrontage: Linear feet of street connected to property

• This feature is a continous feature

plt.scatter(numerical_features['LotFrontage'], target)
plt.xlabel("LotFrontage")
plt.ylabel('SalePrice')

Text(0, 0.5, 'SalePrice')

• helw awy alklam da seems that on average a higher LotFrontage results in a higher SalePrice

LotArea: Lot size in square feet

• This feature is a continous feature

plt.scatter(numerical_features['LotArea'], target)
plt.xlabel("LotArea")
plt.ylabel('SalePrice')

Text(0, 0.5, 'SalePrice')

• Seems that bigger LotArea doesn't mean a higher saleprice

OverallQual feature: Rates the overall material and finish of the house

• from it's histogram distribution it seems that it's a categorical variable
• Each unique number idenity rating of house material & finish
• This feature should be changed into categorical in feature engineering

sns.boxplot(x = numerical_features['OverallQual'], y = target)
cat_feat.append('OverallQual')

• That looks like a Linear relationship between quality and saleprice, which make sense since that people normally like houses with better quality
• This is going to be an important feature for sure

OverallCond: Rates the overall condition of the house

• from it's histogram distribution it seems that it's a categorical variable
• Each unique number idenity rating of house condition
• This feature should be changed into categorical in feature engineering

sns.boxplot(x=numerical_features['OverallCond'], y=target)
cat_feat.append('OverallCond')

• Better OverallCond seems to usually have higher prices that house with lower overallcond, but the relation isn't as strong as overallqual feature

YearBuilt: Original construction date

• Even though there is a lot of values in year built feature, it's still considered a categorical variable
• This feature should be changed into a categorical feature (maybe calculate building age??)(maybe use YearRemodAdd to calculate something?)

plt.scatter(numerical_features['YearBuilt'], target)
plt.xlabel('YearBuilt')
plt.ylabel('SalePrice')
cat_feat.append('YearBuilt')

• Year built seems to have positive relation with Saleprice, newer buildings on average have higher prices

YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)

• Even though there is a lot of values in year built feature, it's still considered a categorical variable
• This feature should be changed into a categorical feature (Maybe it can help in better estimating house age?)
• (Maybe compare between houses that got remodeled and those that didn't?)

plt.scatter(numerical_features['YearRemodAdd'], target)
plt.xlabel('YearRemodAdd')
plt.ylabel('SalePrice')
cat_feat.append('YearRemodAdd')

• On average it seems that sale price increases the later the remodeling occured

MasVnrArea: Masonry veneer area in square feet

• This feature is a continous variable

plt.scatter(numerical_features['MasVnrArea'], target)
plt.xlabel('MasVnrArea')
plt.ylabel('SalePrice')

Text(0, 0.5, 'SalePrice')

• From scatterplot above, it seems that saleprice isn't affected by MasVnrArea

TotalBsmtSF: Total square feet of basement area

• This Feature is a continous variable

plt.scatter(numerical_features['TotalBsmtSF'], target)
plt.xlabel('TotalBxmtSF')
plt.ylabel('SalePrice')

Text(0, 0.5, 'SalePrice')

• There is a linear relation between SalePrice & Basement size, Bigger basements usually means higher prices

1stFlrSF: First Floor square feet 2ndFlrSF: Second floor square feet

• Those features are continous variables

plt.scatter(numerical_features['1stFlrSF'],target)
plt.xlabel('1stFlrSFF')
plt.ylabel('SalePrice')
plt.show()
plt.scatter(numerical_features['2ndFlrSF'],target)
plt.xlabel('2ndFlrSFF')
plt.ylabel('SalePrice')
plt.show()

• There is a linear relation between size of a floor & Sale price
• A lot of zero values in 2nd floor means that there is no 2nd floor in this house

LowQualFinSF: Low quality finished square feet (all floors)

• Most values are zeros, it would be better to drop this feature

GrLivArea: Above grade (ground) living area square feet

• This feature is a continous feature

plt.scatter(numerical_features['GrLivArea'],target)
plt.xlabel('GrLivArea')
plt.ylabel('SalePrice')
plt.show()

• Eh al7lawa de, this scatter plot clearly show a strong linear relation between SalePrice & GrLivArea

BsmtFullBath: Basement full bathrooms

• This feature is a categorical feature
• BsmtFullBath Causes prices to increase by a slight margin on average
• BsmtHalfBath doesn't seem to affect price at all

FullBath: Full bathrooms above grade

HalfBath: Half baths above grade

Bedroom: Bedrooms above grade (does NOT include basement bedrooms)

Kitchen: Kitchens above grade

Fireplaces: Number of fireplaces

• All those features are categorical variables

for feature in ['FullBath','HalfBath','BedroomAbvGr','KitchenAbvGr','Fireplaces']:
    sns.boxplot(data = numerical_features,x=feature,y=target)
    plt.show()

    cat_feat.append(feature)

• Having more fullbath seems to increase price
• A halfbath present seems to slightly increase price, with no increase if number of halfbaths increase
• Number of bedrooms seems to slightly increase price on average
• Number of kitchens doesn't seem to affect price that much
• Number of Fireplaces seems to increase price

GarageYrBlt: Year garage was built

• This feature is a continous variable

plt.scatter(numerical_features['GarageYrBlt'],target)
plt.xlabel('GarageYrBlt')
plt.ylabel('SalePrice')
plt.show()

• GarageYrBlt seems to affect sale price (Might be just because newer houses usually have newer garages)

GarageCars: Size of garage in car capacity

• This is a categorical variable

sns.boxplot(x=numerical_features['GarageCars'],y= target)
plt.show()
cat_feat.append('GarageCars')

• Increased garage car capcity seems to have higher prices

GarageArea: Size of garage in square feet

• This is a continous variable

plt.scatter(numerical_features['GarageArea'],target)
plt.xlabel('GarageArea')
plt.ylabel('SalePrice')
plt.show()

• Increased Garage area usually means higher prices (might be that bigger garages usually have bigger car capacities)

for feature in ['WoodDeckSF','OpenPorchSF','EnclosedPorch','3SsnPorch','ScreenPorch','PoolArea']:
    plt.scatter(numerical_features[feature],target)
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.show()
    # cat_feat.append(feature)

• All of previous features doesn't really seem to affect price

MoSold: Month House was sold in

• This is a categorical variable

sns.lineplot(x=numerical_features['MoSold'], y=target)
cat_feat.append('MoSold')

• It seems that houses sold in certain seasons have higher prices than houses sold in other seasons

YrSold: Year Sold (YYYY)

• This is a categorical feature
• (Use this feature to calculate House age??)

sns.lineplot(x=numerical_features['YrSold'], y=target)
cat_feat.append('YrSold')

• Seems that prices peaked in 2006 and decreased in following years probably due to the house marketing crash

def heatmap(df,label=None):
    plt.figure(figsize = (16,10),dpi=300)
    sns.heatmap(df.corr(method='pearson'),cmap='YlOrRd',annot=True,annot_kws={'size':7})
    plt.title(f'Correlations between features of the {label} set')
    plt.show()

heatmap(numerical_features)

• From previous analysis and this heatmap, it seems that [OverallQual, GrLivArea, GarageCars, GarageArea, YearBuilt, YearRemodadd, MasVnrArea] haver greatest effect on sale price

Feature enginnering ideas about numeric features inspired by analysis:

• Create a new feature that combines OverallQuall & OverallCond
• Create a building age feature (YrSold - YearBuilt)
• Create a building age after being remodeled feature (YrSold - YearRemodAdd)
• Turn MSSubClass feature into a categorical feature
• Turn OverallQuall & OverallCond into categorical features (and their combined feature)
• Remove YearBuilt feature when you add building age feature and turn building age feature into a categorical feature
• Turn all features that count things present in house into categorical features

---

elhmdullah khlsna al numeric yala nkhoush 3la category winsha'llah nshoof shwyeet insights helwa

categorical_features.head()

	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	KitchenQual	Functional	FireplaceQu	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
0	RL	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	No	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	NaN	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
1	RL	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	Gable	CompShg	MetalSd	MetalSd	None	TA	TA	CBlock	Gd	TA	Gd	ALQ	Unf	GasA	Ex	Y	SBrkr	TA	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
2	RL	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Mn	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
3	RL	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	Gable	CompShg	Wd Sdng	Wd Shng	None	TA	TA	BrkTil	TA	Gd	No	ALQ	Unf	GasA	Gd	Y	SBrkr	Gd	Typ	Gd	Detchd	Unf	TA	TA	Y	NaN	NaN	NaN	WD	Abnorml
4	RL	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Av	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal

categorical_features['SalePrice'] = target
def category_analyze(feature, target):
    sns.boxplot(data = categorical_features, x=feature, y=target)
    plt.show()

    analyze = pd.DataFrame(data = categorical_features.groupby(feature)[target].agg(['median','mean','count']))
    
    """ 
        how analyze dataframe is created 
        1.'categorical_features.groupby(feature)': This groups the data in the "categorical_features" DataFrame by a column specified as "feature".
        It groups the data into separate groups based on unique values in the "feature" column.
        
        2. '[target]': This selects the column named "target" from the grouped data.
        
        3 '.agg(['median', 'mean', 'count'])': This applies aggregation functions to the "target" column within each group.
        It calculates the median, mean, and count for each group.

        4. 'pd.DataFrame(data=...)': This creates a new DataFrame using the aggregated data. The "data" parameter is set to the result
            of the aggregation performed in the previous step.

        5. analyze = ...: This assigns the newly created DataFrame to the variable "analyze".
    """
    return analyze    

MSZoning : Identifies the general zoning classification of the sale.

category_analyze('MSZoning', 'SalePrice')

	median	mean	count
MSZoning
C (all)	74700.0	74528.000000	10
FV	205950.0	214014.061538	65
RH	136500.0	131558.375000	16
RL	174000.0	191004.994787	1151
RM	120500.0	126316.830275	218

Street: Type of road access to property

category_analyze('Street','SalePrice')

	median	mean	count
Street
Grvl	114250.0	130190.500000	6
Pave	163000.0	181130.538514	1454

• price of paved streets are on average higher than gravel streets

Alley: Type of alley access to property

• NaN values = no alley access

category_analyze('Alley','SalePrice')

	median	mean	count
Alley
Grvl	119500.0	122219.080000	50
Pave	172500.0	168000.585366	41

• Gravel alley access houses has lowest prices compared to houses with no alley access or paved alley

LotShape: General shape of property

category_analyze('LotShape','SalePrice')

	median	mean	count
LotShape
IR1	189000.0	206101.665289	484
IR2	221000.0	239833.365854	41
IR3	203570.0	216036.500000	10
Reg	146000.0	164754.818378	925

• Regular houses seems to be the most popular and the cheapest of the bunch, IR1 also seems to be popular with a higher average price than Regular houses

LandContour: Flatness of the property

category_analyze('LandContour','SalePrice')

	median	mean	count
LandContour
Bnk	139400.0	143104.079365	63
HLS	222250.0	231533.940000	50
Low	190000.0	203661.111111	36
Lvl	162900.0	180183.746758	1311

• Most of the houses are near flat/level
• It seems that Banked houses are the cheapest of the bunch

LotConfig: Lot configuration

category_analyze('LotConfig', 'SalePrice')

	median	mean	count
LotConfig
Corner	160000.0	181623.425856	263
CulDSac	199262.0	223854.617021	94
FR2	165000.0	177934.574468	47
FR3	195450.0	208475.000000	4
Inside	159697.5	176938.047529	1052

• Most of houses have an inside lot, that also happen to be the cheapest of the bunch on average
• Other kinds of lots appear to be higher priced

LandSlope: Slope of property

category_analyze('LandSlope', 'SalePrice')

	median	mean	count
LandSlope
Gtl	161875.0	179956.799566	1382
Mod	186700.0	196734.138462	65
Sev	185000.0	204379.230769	13

• Most of houses have a gentle slope
• Severe slope is very rare

Neighborhood: Physical locations within Ames city limits

category_analyze('Neighborhood', 'SalePrice')
# m3lsh msh 2adr agher al format bta3 alklam alt7t wakhleeh vertical y3ny 3shan ana 3aml function 3shan akhls

	median	mean	count
Neighborhood
Blmngtn	191000.0	194870.882353	17
Blueste	137500.0	137500.000000	2
BrDale	106000.0	104493.750000	16
BrkSide	124300.0	124834.051724	58
ClearCr	200250.0	212565.428571	28
CollgCr	197200.0	197965.773333	150
Crawfor	200624.0	210624.725490	51
Edwards	121750.0	128219.700000	100
Gilbert	181000.0	192854.506329	79
IDOTRR	103000.0	100123.783784	37
MeadowV	88000.0	98576.470588	17
Mitchel	153500.0	156270.122449	49
NAmes	140000.0	145847.080000	225
NPkVill	146000.0	142694.444444	9
NWAmes	182900.0	189050.068493	73
NoRidge	301500.0	335295.317073	41
NridgHt	315000.0	316270.623377	77
OldTown	119000.0	128225.300885	113
SWISU	139500.0	142591.360000	25
Sawyer	135000.0	136793.135135	74
SawyerW	179900.0	186555.796610	59
Somerst	225500.0	225379.837209	86
StoneBr	278000.0	310499.000000	25
Timber	228475.0	242247.447368	38
Veenker	218000.0	238772.727273	11

• Looks like every Neighbourhood is really important when it comes to determining saleprice, as each one have a different average

Condition1: Proximity to various conditions

category_analyze('Condition1','SalePrice')

	median	mean	count
Condition1
Artery	119550.0	135091.666667	48
Feedr	140000.0	142475.481481	81
Norm	166500.0	184495.492063	1260
PosA	212500.0	225875.000000	8
PosN	200000.0	215184.210526	19
RRAe	142500.0	138400.000000	11
RRAn	171495.0	184396.615385	26
RRNe	190750.0	190750.000000	2
RRNn	214000.0	212400.000000	5

Condition2: Proximity to various conditions (if more than one is present)

category_analyze('Condition2','SalePrice')

	median	mean	count
Condition2
Artery	106500.0	106500.000000	2
Feedr	127500.0	121166.666667	6
Norm	163500.0	181169.405536	1445
PosA	325000.0	325000.000000	1
PosN	284875.0	284875.000000	2
RRAe	190000.0	190000.000000	1
RRAn	136905.0	136905.000000	1
RRNn	96750.0	96750.000000	2

BldgType: Type of dwelling

category_analyze('BldgType','SalePrice')

	median	mean	count
BldgType
1Fam	167900.0	185763.807377	1220
2fmCon	127500.0	128432.258065	31
Duplex	135980.0	133541.076923	52
Twnhs	137500.0	135911.627907	43
TwnhsE	172200.0	181959.342105	114

HouseStyle: Style of dwelling

category_analyze('HouseStyle','SalePrice')

	median	mean	count
HouseStyle
1.5Fin	132000.0	143116.740260	154
1.5Unf	111250.0	110150.000000	14
1Story	154750.0	175985.477961	726
2.5Fin	194000.0	220000.000000	8
2.5Unf	133900.0	157354.545455	11
2Story	190000.0	210051.764045	445
SFoyer	135960.0	135074.486486	37
SLvl	164500.0	166703.384615	65

RoofStyle: Type of roof

category_analyze('RoofStyle','SalePrice')

	median	mean	count
RoofStyle
Flat	185000.0	194690.000000	13
Gable	160000.0	171483.956179	1141
Gambrel	139000.0	148909.090909	11
Hip	176500.0	218876.933566	286
Mansard	175000.0	180568.428571	7
Shed	225000.0	225000.000000	2

RoofMatl: Roof material

category_analyze('RoofMatl','SalePrice')

	median	mean	count
RoofMatl
ClyTile	160000.0	160000.000000	1
CompShg	162000.0	179803.679219	1434
Membran	241500.0	241500.000000	1
Metal	180000.0	180000.000000	1
Roll	137000.0	137000.000000	1
Tar&Grv	167000.0	185406.363636	11
WdShake	242000.0	241400.000000	5
WdShngl	332500.0	390250.000000	6

Exterior1st: Exterior covering on house

category_analyze('Exterior1st','SalePrice')

	median	mean	count
Exterior1st
AsbShng	108000.0	107385.550000	20
AsphShn	100000.0	100000.000000	1
BrkComm	71000.0	71000.000000	2
BrkFace	165750.0	194573.000000	50
CBlock	105000.0	105000.000000	1
CemntBd	236500.0	231690.655738	61
HdBoard	149900.0	163077.450450	222
ImStucc	262000.0	262000.000000	1
MetalSd	139000.0	149422.177273	220
Plywood	167450.0	175942.379630	108
Stone	258500.0	258500.000000	2
Stucco	144000.0	162990.000000	25
VinylSd	200000.0	213732.900971	515
Wd Sdng	138943.5	149841.645631	206
WdShing	128700.0	150655.076923	26

Exterior2nd: Exterior covering on house (if more than one material)

category_analyze('Exterior2nd','SalePrice')

	median	mean	count
Exterior2nd
AsbShng	111000.0	114060.550000	20
AsphShn	139000.0	138000.000000	3
Brk Cmn	147000.0	126714.285714	7
BrkFace	160000.0	195818.000000	25
CBlock	105000.0	105000.000000	1
CmentBd	238750.0	230093.833333	60
HdBoard	155000.0	167661.565217	207
ImStucc	187600.0	252070.000000	10
MetalSd	138750.0	149803.172897	214
Other	319000.0	319000.000000	1
Plywood	160750.0	168112.387324	142
Stone	177000.0	158224.800000	5
Stucco	142000.0	155905.153846	26
VinylSd	200070.5	214432.460317	504
Wd Sdng	138000.0	148386.065990	197
Wd Shng	138225.0	161328.947368	38

MasVnrType: Masonry veneer type

category_analyze('MasVnrType','SalePrice')

	median	mean	count
MasVnrType
BrkCmn	139000.0	146318.066667	15
BrkFace	181000.0	204691.871910	445
None	143000.0	156221.891204	864
Stone	246839.0	265583.625000	128

ExterQual: Evaluates the quality of the material on the exterior

category_analyze('ExterQual','SalePrice')

	median	mean	count
ExterQual
Ex	364606.5	367360.961538	52
Fa	82250.0	87985.214286	14
Gd	220000.0	231633.510246	488
TA	139450.0	144341.313466	906

ExterCond: Evaluates the present condition of the material on the exterior

category_analyze('ExterCond','SalePrice')

	median	mean	count
ExterCond
Ex	161000.0	201333.333333	3
Fa	95750.0	102595.142857	28
Gd	151250.0	168897.568493	146
Po	76500.0	76500.000000	1
TA	167370.0	184034.896256	1282

Foundation: Type of foundation

category_analyze('Foundation','SalePrice')

	median	mean	count
Foundation
BrkTil	125250.0	132291.075342	146
CBlock	141500.0	149805.714511	634
PConc	205000.0	225230.442040	647
Slab	104150.0	107365.625000	24
Stone	126500.0	165959.166667	6
Wood	164000.0	185666.666667	3

BsmtQual: Evaluates the height of the basement

category_analyze('BsmtQual','SalePrice')

	median	mean	count
BsmtQual
Ex	318000.0	327041.041322	121
Fa	112000.0	115692.028571	35
Gd	192070.0	202688.478964	618
TA	135500.0	140759.818182	649

BsmtCond: Evaluates the general condition of the basement

category_analyze('BsmtCond','SalePrice')

	median	mean	count
BsmtCond
Fa	118500.0	121809.533333	45
Gd	193879.0	213599.907692	65
Po	64000.0	64000.000000	2
TA	165000.0	183632.620900	1311

BsmtExposure: Refers to walkout or garden level walls

category_analyze('BsmtExposure','SalePrice')

	median	mean	count
BsmtExposure
Av	185850.0	206643.420814	221
Gd	226975.0	257689.805970	134
Mn	182450.0	192789.657895	114
No	154000.0	165652.295908	953

Heating: Type of heating

category_analyze('Heating','SalePrice')

	median	mean	count
Heating
Floor	72500.0	72500.000000	1
GasA	164500.0	182021.195378	1428
GasW	134950.0	166632.166667	18
Grav	79000.0	75271.428571	7
OthW	125750.0	125750.000000	2
Wall	91450.0	92100.000000	4

HeatingQC: Heating quality and condition

category_analyze('HeatingQC','SalePrice')

	median	mean	count
HeatingQC
Ex	194700.0	214914.429150	741
Fa	123500.0	123919.489796	49
Gd	152000.0	156858.871369	241
Po	87000.0	87000.000000	1
TA	135000.0	142362.876168	428

CentralAir: Central air conditioning

category_analyze('CentralAir','SalePrice')

	median	mean	count
CentralAir
N	98000.0	105264.073684	95
Y	168000.0	186186.709890	1365

Electrical: Electrical system

category_analyze('Electrical','SalePrice')

	median	mean	count
Electrical
FuseA	121250.0	122196.893617	94
FuseF	115000.0	107675.444444	27
FuseP	82000.0	97333.333333	3
Mix	67000.0	67000.000000	1
SBrkr	170000.0	186825.113193	1334

KitchenQual: Kitchen quality

category_analyze('KitchenQual','SalePrice')

	median	mean	count
KitchenQual
Ex	316750.0	328554.670000	100
Fa	115000.0	105565.205128	39
Gd	201400.0	212116.023891	586
TA	137000.0	139962.511565	735

Functional: Home functionality (Assume typical unless deductions are warranted)

category_analyze('Functional','SalePrice')

	median	mean	count
Functional
Maj1	140750.0	153948.142857	14
Maj2	85000.0	85800.000000	5
Min1	139000.0	146385.483871	31
Min2	140000.0	144240.647059	34
Mod	137900.0	168393.333333	15
Sev	129000.0	129000.000000	1
Typ	165500.0	183429.147059	1360

FireplaceQu: Fireplace quality

category_analyze('FireplaceQu','SalePrice')

	median	mean	count
FireplaceQu
Ex	314250.0	337712.500000	24
Fa	158000.0	167298.484848	33
Gd	206950.0	226351.415789	380
Po	131500.0	129764.150000	20
TA	187500.0	205723.488818	313

GarageType: Garage location

category_analyze('GarageType','SalePrice')

	median	mean	count
GarageType
2Types	159000.0	151283.333333	6
Attchd	185000.0	202892.656322	870
Basment	148000.0	160570.684211	19
BuiltIn	227500.0	254751.738636	88
CarPort	108000.0	109962.111111	9
Detchd	129500.0	134091.162791	387

GarageFinish: Interior finish of the garage

category_analyze('GarageFinish','SalePrice')

	median	mean	count
GarageFinish
Fin	215000.0	240052.690341	352
RFn	190000.0	202068.869668	422
Unf	135000.0	142156.423140	605

GarageQual: Garage quality

category_analyze('GarageQual','SalePrice')

	median	mean	count
GarageQual
Ex	127500.0	241000.000000	3
Fa	115000.0	123573.354167	48
Gd	209115.0	215860.714286	14
Po	96500.0	100166.666667	3
TA	170000.0	187489.836003	1311

GarageCond: Garage condition

category_analyze('GarageCond','SalePrice')

	median	mean	count
GarageCond
Ex	124000.0	124000.000000	2
Fa	114504.0	114654.028571	35
Gd	148000.0	179930.000000	9
Po	108000.0	108500.000000	7
TA	170000.0	187885.735294	1326

PavedDrive: Paved driveway

category_analyze('PavedDrive','SalePrice')

	median	mean	count
PavedDrive
N	111000.0	115039.122222	90
P	132250.0	132330.000000	30
Y	168500.0	186433.973881	1340

PoolQC: Pool quality

category_analyze('PoolQC','SalePrice')

	median	mean	count
PoolQC
Ex	490000.0	490000.0	2
Fa	215500.0	215500.0	2
Gd	171000.0	201990.0	3

Fence: Fence quality

category_analyze('Fence','SalePrice')

	median	mean	count
Fence
GdPrv	167500.0	178927.457627	59
GdWo	138750.0	140379.314815	54
MnPrv	137450.0	148751.089172	157
MnWw	130000.0	134286.363636	11

MiscFeature: Miscellaneous feature not covered in other categories

category_analyze('MiscFeature','SalePrice')

	median	mean	count
MiscFeature
Gar2	170750.0	170750.000000	2
Othr	94000.0	94000.000000	2
Shed	144000.0	151187.612245	49
TenC	250000.0	250000.000000	1

SaleType: Type of sale

category_analyze('SaleType','SalePrice')

	median	mean	count
SaleType
COD	139000.0	143973.255814	43
CWD	188750.0	210600.000000	4
Con	269600.0	269600.000000	2
ConLD	140000.0	138780.888889	9
ConLI	125000.0	200390.000000	5
ConLw	144000.0	143700.000000	5
New	247453.0	274945.418033	122
Oth	116050.0	119850.000000	3
WD	158000.0	173401.836622	1267

SaleCondition: Condition of sale

category_analyze('SaleCondition','SalePrice')

	median	mean	count
SaleCondition
Abnorml	130000.0	146526.623762	101
AdjLand	104000.0	104125.000000	4
Alloca	148145.0	167377.416667	12
Family	140500.0	149600.000000	20
Normal	160000.0	175202.219533	1198
Partial	244600.0	272291.752000	125

categorical_features.columns

Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',
       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',
       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',
       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',
       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',
       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',
       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',
       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',
       'SaleType', 'SaleCondition', 'SalePrice'],
      dtype='object')

Feature engineering ideas about categorical features inspired from analysis:

• Combine LotShape & LotConfig into a new feature
• Combine Condition1 & Condition2 into a new feature
• combine RoofStyle & RoofMatl into a new feature
• Combine Exterior1st, Exterior2ndm ExterQual & ExterCond intoa anew feature
• Combine BsmtQual & BsmtCond into a new feature
• Combine Heating & HeatingQC into a new feature
• Combine Heating & CentralAir into a new feature
• Combine GarageType, GarageCars & GarageArea into a new feature
• Combine SaleType & SaleCondition into a new feature

---

Combin Train and Test Sets

target = train0['SalePrice'] # ana 3yzo m3aya f ana hkhzno dlw2ty 3shan h3mlo drop
test_ids = test0['Id'] # wda ana 3yzo brdo f khzno m3aya 3shan al sample sumbmissions

train1 = train0.drop(['Id', 'SalePrice'], axis=1)
test1 = test0.drop('Id', axis=1)

# tb hwa ana leeh a3ml concat, hyzwd al accuracy bt3ty wna bfill al missing data
data1 = pd.concat([train1,test1], axis=0).reset_index(drop=True)
data1

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706.0	Unf	0.0	150.0	856.0	GasA	Ex	Y	SBrkr	856	854	0	1710	1.0	0.0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2.0	548.0	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal
1	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978.0	Unf	0.0	284.0	1262.0	GasA	Ex	Y	SBrkr	1262	0	0	1262	0.0	1.0	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2.0	460.0	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal
2	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486.0	Unf	0.0	434.0	920.0	GasA	Ex	Y	SBrkr	920	866	0	1786	1.0	0.0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2.0	608.0	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal
3	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216.0	Unf	0.0	540.0	756.0	GasA	Gd	Y	SBrkr	961	756	0	1717	1.0	0.0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3.0	642.0	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml
4	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655.0	Unf	0.0	490.0	1145.0	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1.0	0.0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3.0	836.0	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2914	160	RM	21.0	1936	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	MeadowV	Norm	Norm	Twnhs	2Story	4	7	1970	1970	Gable	CompShg	CemntBd	CmentBd	None	0.0	TA	TA	CBlock	TA	TA	No	Unf	0.0	Unf	0.0	546.0	546.0	GasA	Gd	Y	SBrkr	546	546	0	1092	0.0	0.0	1	1	3	1	TA	5	Typ	0	NaN	NaN	NaN	NaN	0.0	0.0	NaN	NaN	Y	0	0	0	0	0	0	NaN	NaN	NaN	0	6	2006	WD	Normal
2915	160	RM	21.0	1894	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	MeadowV	Norm	Norm	TwnhsE	2Story	4	5	1970	1970	Gable	CompShg	CemntBd	CmentBd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	252.0	Unf	0.0	294.0	546.0	GasA	TA	Y	SBrkr	546	546	0	1092	0.0	0.0	1	1	3	1	TA	6	Typ	0	NaN	CarPort	1970.0	Unf	1.0	286.0	TA	TA	Y	0	24	0	0	0	0	NaN	NaN	NaN	0	4	2006	WD	Abnorml
2916	20	RL	160.0	20000	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	Mitchel	Norm	Norm	1Fam	1Story	5	7	1960	1996	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	ALQ	1224.0	Unf	0.0	0.0	1224.0	GasA	Ex	Y	SBrkr	1224	0	0	1224	1.0	0.0	1	0	4	1	TA	7	Typ	1	TA	Detchd	1960.0	Unf	2.0	576.0	TA	TA	Y	474	0	0	0	0	0	NaN	NaN	NaN	0	9	2006	WD	Abnorml
2917	85	RL	62.0	10441	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	Mitchel	Norm	Norm	1Fam	SFoyer	5	5	1992	1992	Gable	CompShg	HdBoard	Wd Shng	None	0.0	TA	TA	PConc	Gd	TA	Av	GLQ	337.0	Unf	0.0	575.0	912.0	GasA	TA	Y	SBrkr	970	0	0	970	0.0	1.0	1	0	3	1	TA	6	Typ	0	NaN	NaN	NaN	NaN	0.0	0.0	NaN	NaN	Y	80	32	0	0	0	0	NaN	MnPrv	Shed	700	7	2006	WD	Normal
2918	60	RL	74.0	9627	Pave	NaN	Reg	Lvl	AllPub	Inside	Mod	Mitchel	Norm	Norm	1Fam	2Story	7	5	1993	1994	Gable	CompShg	HdBoard	HdBoard	BrkFace	94.0	TA	TA	PConc	Gd	TA	Av	LwQ	758.0	Unf	0.0	238.0	996.0	GasA	Ex	Y	SBrkr	996	1004	0	2000	0.0	0.0	2	1	3	1	TA	9	Typ	1	TA	Attchd	1993.0	Fin	3.0	650.0	TA	TA	Y	190	48	0	0	0	0	NaN	NaN	NaN	0	11	2006	WD	Normal

2919 rows × 79 columns

Cleaning and Missing Values

Ensure proper Data Types

data1.isna().sum()

MSSubClass          0
MSZoning            4
LotFrontage       486
LotArea             0
Street              0
Alley            2721
LotShape            0
LandContour         0
Utilities           2
LotConfig           0
LandSlope           0
Neighborhood        0
Condition1          0
Condition2          0
BldgType            0
HouseStyle          0
OverallQual         0
OverallCond         0
YearBuilt           0
YearRemodAdd        0
RoofStyle           0
RoofMatl            0
Exterior1st         1
Exterior2nd         1
MasVnrType         24
MasVnrArea         23
ExterQual           0
ExterCond           0
Foundation          0
BsmtQual           81
BsmtCond           82
BsmtExposure       82
BsmtFinType1       79
BsmtFinSF1          1
BsmtFinType2       80
BsmtFinSF2          1
BsmtUnfSF           1
TotalBsmtSF         1
Heating             0
HeatingQC           0
CentralAir          0
Electrical          1
1stFlrSF            0
2ndFlrSF            0
LowQualFinSF        0
GrLivArea           0
BsmtFullBath        2
BsmtHalfBath        2
FullBath            0
HalfBath            0
BedroomAbvGr        0
KitchenAbvGr        0
KitchenQual         1
TotRmsAbvGrd        0
Functional          2
Fireplaces          0
FireplaceQu      1420
GarageType        157
GarageYrBlt       159
GarageFinish      159
GarageCars          1
GarageArea          1
GarageQual        159
GarageCond        159
PavedDrive          0
WoodDeckSF          0
OpenPorchSF         0
EnclosedPorch       0
3SsnPorch           0
ScreenPorch         0
PoolArea            0
PoolQC           2909
Fence            2348
MiscFeature      2814
MiscVal             0
MoSold              0
YrSold              0
SaleType            1
SaleCondition       0
dtype: int64

data2 = data1.copy()

# msh 3arf leeh a7na hwlnaha l string bs insha'llah h3rf 2odam
# tmm 3rft hwlnaha object 3shan hwa koul al object category f 22dr amlahom bal mod 3la toul whya kant category b numbers
for feature in cat_feat:
    data2[feature] = data2[feature].astype(str) 

data2.select_dtypes(np.number)

	LotFrontage	LotArea	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	TotRmsAbvGrd	GarageYrBlt	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal
0	65.0	8450	196.0	706.0	0.0	150.0	856.0	856	854	0	1710	1.0	0.0	8	2003.0	548.0	0	61	0	0	0	0	0
1	80.0	9600	0.0	978.0	0.0	284.0	1262.0	1262	0	0	1262	0.0	1.0	6	1976.0	460.0	298	0	0	0	0	0	0
2	68.0	11250	162.0	486.0	0.0	434.0	920.0	920	866	0	1786	1.0	0.0	6	2001.0	608.0	0	42	0	0	0	0	0
3	60.0	9550	0.0	216.0	0.0	540.0	756.0	961	756	0	1717	1.0	0.0	7	1998.0	642.0	0	35	272	0	0	0	0
4	84.0	14260	350.0	655.0	0.0	490.0	1145.0	1145	1053	0	2198	1.0	0.0	9	2000.0	836.0	192	84	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2914	21.0	1936	0.0	0.0	0.0	546.0	546.0	546	546	0	1092	0.0	0.0	5	NaN	0.0	0	0	0	0	0	0	0
2915	21.0	1894	0.0	252.0	0.0	294.0	546.0	546	546	0	1092	0.0	0.0	6	1970.0	286.0	0	24	0	0	0	0	0
2916	160.0	20000	0.0	1224.0	0.0	0.0	1224.0	1224	0	0	1224	1.0	0.0	7	1960.0	576.0	474	0	0	0	0	0	0
2917	62.0	10441	0.0	337.0	0.0	575.0	912.0	970	0	0	970	0.0	1.0	6	NaN	0.0	80	32	0	0	0	0	700
2918	74.0	9627	94.0	758.0	0.0	238.0	996.0	996	1004	0	2000	0.0	0.0	9	1993.0	650.0	190	48	0	0	0	0	0

2919 rows × 23 columns

Fill Categorical Missing Values

# tmm 3ayz ashouf koul al object columns al 3ndhom missing values
data2.select_dtypes('object').loc[:, data2.isna().sum() > 0].columns

Index(['MSZoning', 'Alley', 'Utilities', 'Exterior1st', 'Exterior2nd',
       'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
       'BsmtFinType2', 'Electrical', 'KitchenQual', 'Functional',
       'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond',
       'PoolQC', 'Fence', 'MiscFeature', 'SaleType'],
      dtype='object')

# Impute using a constant value
# da al NA feeh bykoun leeh meaning f msh hynf3 a amllah zaee msln mfeesh krasy sofra:) f msh hynf3 amla mknha hmam y3ny
for column in [
    'Alley',
    'BsmtQual',
    'BsmtCond',
    'BsmtExposure',
    'BsmtFinType1',
    'BsmtFinType2',
    'FireplaceQu',
    'GarageType',
    'GarageFinish',
    'GarageQual',
    'GarageCond',
    'PoolQC',
    'Fence',
    'MiscFeature'
]:
    data2[column] = data2[column].fillna("None")
# Impute using the column mode
for column in [
    'MSZoning',
    'Utilities',
    'Exterior1st',
    'Exterior2nd',
    'MasVnrType',
    'Electrical',
    'KitchenQual',
    'Functional',
    'SaleType'
]:
    data2[column] = data2[column].fillna(data2[column].mode()[0])

# we have no missing values in categore colums
data2.select_dtypes('object').isna().sum().sum()

0

data2.select_dtypes(np.number).isna().sum().sum()

677

data3 = data2.copy()

Numeric Missing Values

Exploring Numerical Features

data3.select_dtypes(np.number).loc[:, data3.isna().sum() > 0].columns

Index(['LotFrontage', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',
       'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageYrBlt',
       'GarageArea'],
      dtype='object')

def Knn_impute(df, na_target): # na_target -> column have null values
    """ 
        Take data frame and column
        return data frame with filling missing value in column
        by using Knn algorithm
    """
    df = df.copy()

    numeric_df = df.select_dtypes(np.number)
    non_na_columns = numeric_df.loc[:,numeric_df.isna().sum() == 0].columns

    # y_train: rows that not have null values in column(na_target)
    y_train = numeric_df.loc[numeric_df[na_target].isna() == False, na_target]
    # X_train: columns that not have null values (non_na_column) & select from these column rows that not have null values
    X_train = numeric_df.loc[numeric_df[na_target].isna() == False, non_na_columns]
    # X_test:  columns that not have null values (non_na_column) & select from these column rows that have null values
    X_test = numeric_df.loc[numeric_df[na_target].isna() == True, non_na_columns]

    """ 
    KNN Regressor vs classifier
    The key differences are: KNN regression tries to predict the value of the output variable by using a local average.
    KNN classification attempts to predict the class to which the output variable belong by computing the local probability. 
    """
    # fa7na mstkhdmeen KneKNeighborsRegressor 3shan hya numeric value
    knn = KNeighborsRegressor()
    knn.fit(X_train, y_train)

    # hna hyrg3 al perdict ll null values alna kount 3ayz amlaha
    y_pred = knn.predict(X_test)
    
    df.loc[df[na_target].isna() == True, na_target] = y_pred

    return df

for column in [
    'LotFrontage',
    'MasVnrArea',
    'BsmtFinSF1',
    'BsmtFinSF2',
    'BsmtUnfSF',
    'TotalBsmtSF',
    'BsmtFullBath',
    'BsmtHalfBath',
    'GarageYrBlt',
    'GarageArea',
]:
    data3 = Knn_impute(data3,column)

data3.isna().sum().sum()

0

data4 = data3.copy()

Feature Engineering

• as we said before what will do in this part msh h3eed alklam tany b2a arg3 foo2 wshoof alhgat alshofnaha

data4['OverallQual'] = data4['OverallQual']
data4['OverallCond'] = data4['OverallCond']
data4['OverallQual-Cond'] = data4['OverallQual'].astype(int) * data4['OverallCond'].astype(int)
data4['Age'] = (data4['YrSold'].astype(int) - data4['YearBuilt'].astype(int)).astype(str)
data4['Age'] = data4['Age']
data4['RemodAge'] = data4['YrSold'].astype(int) - data4['YearRemodAdd'].astype(int)
data4['RemodAge'] = data4['RemodAge']
data4['MSSubClass'] = data4['MSSubClass'].astype(object) 
data4['LotShape-Config'] = data4['LotShape'] + data4['LotConfig']
data4['Condition1-2'] = data4['Condition1'] + data4['Condition2']
data4['RoofStyle-Matl'] = data4['RoofStyle'] + data4['RoofMatl']
data4['Exterior1st-2nd-qual-cond'] = data4['Exterior1st'] + data4['Exterior2nd']+ data4['ExterQual'] + data4['ExterCond']
data4['BsmtQual-BsmtCond'] = data4['BsmtQual'] + data4['BsmtCond']
data4['Heating-HeatingQC'] = data4['Heating'] + data4['HeatingQC']
data4['Heating-CentralAir'] = data4['Heating'] + data4['CentralAir']
data4['SaleType-SaleCondition'] = data4['SaleType'] + data4['SaleCondition']

data5 = data4.copy()

Feature Transformations

Log Transform for Skewed Features

• The log transformation is often used to reduce skewness of a measurement variable

print("Skewness: %f" % train0['SalePrice'].skew())

Skewness: 1.882876

# scipy.stats.skew(data5.select_dtypes(np.number))
skew_df = pd.DataFrame(data5.select_dtypes(np.number).columns, columns=['Feature'])
skew_df['Skew'] = skew_df['Feature'].apply(lambda feature: scipy.stats.skew(data5[feature]))
skew_df['Absolute Skew'] = abs(skew_df['Skew'])
skew_df['Skewed'] = skew_df['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)
skew_df

	Feature	Skew	Absolute Skew	Skewed
0	LotFrontage	1.341483	1.341483	True
1	LotArea	12.822431	12.822431	True
2	MasVnrArea	2.603682	2.603682	True
3	BsmtFinSF1	1.425516	1.425516	True
4	BsmtFinSF2	4.146111	4.146111	True
5	BsmtUnfSF	0.919193	0.919193	True
6	TotalBsmtSF	1.162791	1.162791	True
7	1stFlrSF	1.469604	1.469604	True
8	2ndFlrSF	0.861675	0.861675	True
9	LowQualFinSF	12.088761	12.088761	True
10	GrLivArea	1.269358	1.269358	True
11	BsmtFullBath	0.624451	0.624451	True
12	BsmtHalfBath	3.931594	3.931594	True
13	TotRmsAbvGrd	0.758367	0.758367	True
14	GarageYrBlt	-0.346583	0.346583	False
15	GarageArea	0.240896	0.240896	False
16	WoodDeckSF	1.842433	1.842433	True
17	OpenPorchSF	2.535114	2.535114	True
18	EnclosedPorch	4.003891	4.003891	True
19	3SsnPorch	11.376065	11.376065	True
20	ScreenPorch	3.946694	3.946694	True
21	PoolArea	16.898328	16.898328	True
22	MiscVal	21.947195	21.947195	True

• Features before transformation

data5.select_dtypes(np.number).hist(figsize=(16,20), bins=50, xlabelsize=8, ylabelsize=8);

skew_df['Skewed'].value_counts()

True     21
False     2
Name: Skewed, dtype: int64

data5[skew_df.query("Skewed == True")['Feature'].values].describe()

	LotFrontage	LotArea	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	TotRmsAbvGrd	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal
count	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000	2919.000000
mean	4.211524	9.094915	2.109776	4.226730	0.682190	5.627455	6.733148	7.004186	2.829584	0.077100	7.261502	0.293671	0.042111	1.986955	2.449664	2.333006	0.757435	0.066197	0.451214	0.027361	0.234767
std	0.341178	0.509882	2.622741	2.980867	1.879238	1.866527	1.175875	0.322650	3.277023	0.659472	0.324750	0.351814	0.167439	0.206864	2.592335	2.154239	1.771894	0.587089	1.459808	0.410265	1.240206
min	3.091042	7.170888	0.000000	0.000000	0.000000	0.000000	0.000000	5.814131	0.000000	0.000000	5.814131	0.000000	0.000000	1.098612	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	4.110874	8.919854	0.000000	0.000000	0.000000	5.398163	6.677083	6.776507	0.000000	0.000000	7.027315	0.000000	0.000000	1.791759	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	4.262680	9.154193	0.000000	5.910797	0.000000	6.148468	6.897705	6.987490	0.000000	0.000000	7.275865	0.000000	0.000000	1.945910	0.000000	3.295837	0.000000	0.000000	0.000000	0.000000	0.000000
75%	4.396915	9.356257	5.105945	6.598509	0.000000	6.692083	7.172425	7.235979	6.558198	0.000000	7.464223	0.693147	0.000000	2.079442	5.129899	4.262680	0.000000	0.000000	0.000000	0.000000	0.000000
max	5.749393	12.279537	7.378384	8.638525	7.331060	7.756623	8.717846	8.536211	7.633370	6.970730	8.638171	1.386294	1.098612	2.772589	7.261927	6.610696	6.920672	6.232448	6.357842	6.685861	9.741027

# shif log by 1 np.log1p()
for column in skew_df.query("Skewed == True")['Feature'].values:
    data5[column] = np.log1p(data5[column])

• After log transformation zbt al data bt3ty wkhlaha tkoun normaly distrbuted w mtwz3a zaee alfoll

data5.select_dtypes(np.number).hist(figsize=(16,20), bins=50, xlabelsize=8, ylabelsize=8);

skew_df = pd.DataFrame(data5.select_dtypes(np.number).columns, columns=['Feature'])
skew_df['Skew'] = skew_df['Feature'].apply(lambda feature: scipy.stats.skew(data5[feature]))
skew_df['Absolute Skew'] = abs(skew_df['Skew'])
skew_df['Skewed'] = skew_df['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)
skew_df

	Feature	Skew	Absolute Skew	Skewed
0	LotFrontage	-0.930231	0.930231	True
1	LotArea	-0.504751	0.504751	True
2	MasVnrArea	0.511138	0.511138	True
3	BsmtFinSF1	-0.618036	0.618036	True
4	BsmtFinSF2	2.458996	2.458996	True
5	BsmtUnfSF	-2.159195	2.159195	True
6	TotalBsmtSF	-4.981655	4.981655	True
7	1stFlrSF	0.064828	0.064828	False
8	2ndFlrSF	0.305049	0.305049	False
9	LowQualFinSF	8.557690	8.557690	True
10	GrLivArea	0.013188	0.013188	False
11	BsmtFullBath	0.424532	0.424532	False
12	BsmtHalfBath	3.774762	3.774762	True
13	TotRmsAbvGrd	0.035107	0.035107	False
14	GarageYrBlt	-0.346583	0.346583	False
15	GarageArea	0.240896	0.240896	False
16	WoodDeckSF	0.158033	0.158033	False
17	OpenPorchSF	-0.041797	0.041797	False
18	EnclosedPorch	1.961081	1.961081	True
19	3SsnPorch	8.825256	8.825256	True
20	ScreenPorch	2.945905	2.945905	True
21	PoolArea	14.998335	14.998335	True
22	MiscVal	5.213984	5.213984	True

skew_df['Skewed'].value_counts() # lsa 3ndna shwyeet skeyed feature bs y3ny log transformation helwa wzbtt hgat ktera

True     14
False     9
Name: Skewed, dtype: int64

data4['MoSold'].unique()

array(['2', '5', '9', '12', '10', '8', '11', '4', '1', '7', '3', '6'],
      dtype=object)

Cosine Transform for cyclical Features

• $-cos(0.5236 x)$

# cos transformation 0.5236 3shan akhly al one cycle mn 1 to 12 f 23dt agrb lghaeyt ml2eet al c tb2a 0.5236
data5['MoSold'] = -np.cos(0.5236 * data4['MoSold'].astype(int))
# sns.lineplot(data5,x='MoSold',y=target)
data5

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	60	RL	4.189655	9.042040	Pave	None	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	5.283204	Gd	TA	PConc	Gd	TA	No	GLQ	6.561031	Unf	0.0	5.017280	6.753438	GasA	Ex	Y	SBrkr	6.753438	6.751101	0.0	7.444833	0.693147	0.000000	2	1	3	1	Gd	2.197225	Typ	0	None	Attchd	2003.0	RFn	2.0	548.0	TA	TA	Y	0.000000	4.127134	0.000000	0.0	0.0	0.0	None	None	None	0.000000	-0.499998	2008	WD	Normal
1	20	RL	4.394449	9.169623	Pave	None	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.000000	TA	TA	CBlock	Gd	TA	Gd	ALQ	6.886532	Unf	0.0	5.652489	7.141245	GasA	Ex	Y	SBrkr	7.141245	0.000000	0.0	7.141245	0.000000	0.693147	2	0	3	1	TA	1.945910	Typ	1	TA	Attchd	1976.0	RFn	2.0	460.0	TA	TA	Y	5.700444	0.000000	0.000000	0.0	0.0	0.0	None	None	None	0.000000	0.866028	2007	WD	Normal
2	60	RL	4.234107	9.328212	Pave	None	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	5.093750	Gd	TA	PConc	Gd	TA	Mn	GLQ	6.188264	Unf	0.0	6.075346	6.825460	GasA	Ex	Y	SBrkr	6.825460	6.765039	0.0	7.488294	0.693147	0.000000	2	1	3	1	Gd	1.945910	Typ	1	TA	Attchd	2001.0	RFn	2.0	608.0	TA	TA	Y	0.000000	3.761200	0.000000	0.0	0.0	0.0	None	None	None	0.000000	-0.000011	2008	WD	Normal
3	70	RL	4.110874	9.164401	Pave	None	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.000000	TA	TA	BrkTil	TA	Gd	No	ALQ	5.379897	Unf	0.0	6.293419	6.629363	GasA	Gd	Y	SBrkr	6.869014	6.629363	0.0	7.448916	0.693147	0.000000	1	0	3	1	Gd	2.079442	Typ	1	Gd	Detchd	1998.0	Unf	3.0	642.0	TA	TA	Y	0.000000	3.583519	5.609472	0.0	0.0	0.0	None	None	None	0.000000	-0.499998	2006	WD	Abnorml
4	60	RL	4.442651	9.565284	Pave	None	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	5.860786	Gd	TA	PConc	Gd	TA	Av	GLQ	6.486161	Unf	0.0	6.196444	7.044033	GasA	Ex	Y	SBrkr	7.044033	6.960348	0.0	7.695758	0.693147	0.000000	2	1	4	1	Gd	2.302585	Typ	1	TA	Attchd	2000.0	RFn	3.0	836.0	TA	TA	Y	5.262690	4.442651	0.000000	0.0	0.0	0.0	None	None	None	0.000000	-1.000000	2008	WD	Normal
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2914	160	RM	3.091042	7.568896	Pave	None	Reg	Lvl	AllPub	Inside	Gtl	MeadowV	Norm	Norm	Twnhs	2Story	4	7	1970	1970	Gable	CompShg	CemntBd	CmentBd	None	0.000000	TA	TA	CBlock	TA	TA	No	Unf	0.000000	Unf	0.0	6.304449	6.304449	GasA	Gd	Y	SBrkr	6.304449	6.304449	0.0	6.996681	0.000000	0.000000	1	1	3	1	TA	1.791759	Typ	0	None	None	1973.8	None	0.0	0.0	None	None	Y	0.000000	0.000000	0.000000	0.0	0.0	0.0	None	None	None	0.000000	1.000000	2006	WD	Normal
2915	160	RM	3.091042	7.546974	Pave	None	Reg	Lvl	AllPub	Inside	Gtl	MeadowV	Norm	Norm	TwnhsE	2Story	4	5	1970	1970	Gable	CompShg	CemntBd	CmentBd	None	0.000000	TA	TA	CBlock	TA	TA	No	Rec	5.533389	Unf	0.0	5.686975	6.304449	GasA	TA	Y	SBrkr	6.304449	6.304449	0.0	6.996681	0.000000	0.000000	1	1	3	1	TA	1.945910	Typ	0	None	CarPort	1970.0	Unf	1.0	286.0	TA	TA	Y	0.000000	3.218876	0.000000	0.0	0.0	0.0	None	None	None	0.000000	0.500004	2006	WD	Abnorml
2916	20	RL	5.081404	9.903538	Pave	None	Reg	Lvl	AllPub	Inside	Gtl	Mitchel	Norm	Norm	1Fam	1Story	5	7	1960	1996	Gable	CompShg	VinylSd	VinylSd	None	0.000000	TA	TA	CBlock	TA	TA	No	ALQ	7.110696	Unf	0.0	0.000000	7.110696	GasA	Ex	Y	SBrkr	7.110696	0.000000	0.0	7.110696	0.693147	0.000000	1	0	4	1	TA	2.079442	Typ	1	TA	Detchd	1960.0	Unf	2.0	576.0	TA	TA	Y	6.163315	0.000000	0.000000	0.0	0.0	0.0	None	None	None	0.000000	-0.000011	2006	WD	Abnorml
2917	85	RL	4.143135	9.253591	Pave	None	Reg	Lvl	AllPub	Inside	Gtl	Mitchel	Norm	Norm	1Fam	SFoyer	5	5	1992	1992	Gable	CompShg	HdBoard	Wd Shng	None	0.000000	TA	TA	PConc	Gd	TA	Av	GLQ	5.823046	Unf	0.0	6.356108	6.816736	GasA	TA	Y	SBrkr	6.878326	0.000000	0.0	6.878326	0.000000	0.693147	1	0	3	1	TA	1.945910	Typ	0	None	None	1966.2	None	0.0	0.0	None	None	Y	4.394449	3.496508	0.000000	0.0	0.0	0.0	None	MnPrv	Shed	6.552508	0.866021	2006	WD	Normal
2918	60	RL	4.317488	9.172431	Pave	None	Reg	Lvl	AllPub	Inside	Mod	Mitchel	Norm	Norm	1Fam	2Story	7	5	1993	1994	Gable	CompShg	HdBoard	HdBoard	BrkFace	4.553877	TA	TA	PConc	Gd	TA	Av	LwQ	6.632002	Unf	0.0	5.476464	6.904751	GasA	Ex	Y	SBrkr	6.904751	6.912743	0.0	7.601402	0.000000	0.000000	2	1	3	1	TA	2.302585	Typ	1	TA	Attchd	1993.0	Fin	3.0	650.0	TA	TA	Y	5.252273	3.891820	0.000000	0.0	0.0	0.0	None	None	None	0.000000	-0.866032	2006	WD	Normal

2919 rows × 79 columns

data6 = data5.copy()

Encode Categoricals

data6 = pd.get_dummies(data6) # one hot encodeing 
data6

	LotFrontage	LotArea	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	TotRmsAbvGrd	GarageYrBlt	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	MSSubClass_120	MSSubClass_150	MSSubClass_160	MSSubClass_180	MSSubClass_190	MSSubClass_20	MSSubClass_30	MSSubClass_40	MSSubClass_45	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSSubClass_75	MSSubClass_80	MSSubClass_85	MSSubClass_90	MSZoning_C (all)	MSZoning_FV	MSZoning_RH	MSZoning_RL	MSZoning_RM	Street_Grvl	Street_Pave	Alley_Grvl	Alley_None	Alley_Pave	LotShape_IR1	LotShape_IR2	LotShape_IR3	LotShape_Reg	LandContour_Bnk	LandContour_HLS	LandContour_Low	LandContour_Lvl	Utilities_AllPub	Utilities_NoSeWa	LotConfig_Corner	LotConfig_CulDSac	LotConfig_FR2	LotConfig_FR3	LotConfig_Inside	LandSlope_Gtl	LandSlope_Mod	LandSlope_Sev	Neighborhood_Blmngtn	Neighborhood_Blueste	Neighborhood_BrDale	Neighborhood_BrkSide	Neighborhood_ClearCr	Neighborhood_CollgCr	Neighborhood_Crawfor	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_IDOTRR	Neighborhood_MeadowV	Neighborhood_Mitchel	Neighborhood_NAmes	Neighborhood_NPkVill	Neighborhood_NWAmes	Neighborhood_NoRidge	Neighborhood_NridgHt	Neighborhood_OldTown	Neighborhood_SWISU	Neighborhood_Sawyer	Neighborhood_SawyerW	Neighborhood_Somerst	Neighborhood_StoneBr	Neighborhood_Timber	Neighborhood_Veenker	Condition1_Artery	Condition1_Feedr	Condition1_Norm	Condition1_PosA	Condition1_PosN	Condition1_RRAe	Condition1_RRAn	Condition1_RRNe	Condition1_RRNn	Condition2_Artery	Condition2_Feedr	Condition2_Norm	Condition2_PosA	Condition2_PosN	Condition2_RRAe	Condition2_RRAn	Condition2_RRNn	BldgType_1Fam	BldgType_2fmCon	BldgType_Duplex	BldgType_Twnhs	BldgType_TwnhsE	HouseStyle_1.5Fin	HouseStyle_1.5Unf	HouseStyle_1Story	HouseStyle_2.5Fin	HouseStyle_2.5Unf	HouseStyle_2Story	HouseStyle_SFoyer	HouseStyle_SLvl	OverallQual_1	OverallQual_10	OverallQual_2	OverallQual_3	OverallQual_4	OverallQual_5	OverallQual_6	OverallQual_7	OverallQual_8	OverallQual_9	OverallCond_1	OverallCond_2	OverallCond_3	OverallCond_4	OverallCond_5	OverallCond_6	OverallCond_7	OverallCond_8	OverallCond_9	YearBuilt_1872	YearBuilt_1875	YearBuilt_1879	YearBuilt_1880	YearBuilt_1882	YearBuilt_1885	YearBuilt_1890	YearBuilt_1892	YearBuilt_1893	YearBuilt_1895	YearBuilt_1896	YearBuilt_1898	YearBuilt_1900	YearBuilt_1901	YearBuilt_1902	YearBuilt_1904	YearBuilt_1905	YearBuilt_1906	YearBuilt_1907	YearBuilt_1908	YearBuilt_1910	YearBuilt_1911	YearBuilt_1912	YearBuilt_1913	YearBuilt_1914	YearBuilt_1915	YearBuilt_1916	YearBuilt_1917	YearBuilt_1918	YearBuilt_1919	YearBuilt_1920	YearBuilt_1921	YearBuilt_1922	YearBuilt_1923	YearBuilt_1924	YearBuilt_1925	YearBuilt_1926	YearBuilt_1927	YearBuilt_1928	YearBuilt_1929	YearBuilt_1930	YearBuilt_1931	YearBuilt_1932	YearBuilt_1934	YearBuilt_1935	YearBuilt_1936	YearBuilt_1937	YearBuilt_1938	YearBuilt_1939	YearBuilt_1940	YearBuilt_1941	YearBuilt_1942	YearBuilt_1945	YearBuilt_1946	YearBuilt_1947	YearBuilt_1948	YearBuilt_1949	YearBuilt_1950	YearBuilt_1951	YearBuilt_1952	YearBuilt_1953	YearBuilt_1954	YearBuilt_1955	YearBuilt_1956	YearBuilt_1957	YearBuilt_1958	YearBuilt_1959	YearBuilt_1960	YearBuilt_1961	YearBuilt_1962	YearBuilt_1963	YearBuilt_1964	YearBuilt_1965	YearBuilt_1966	YearBuilt_1967	YearBuilt_1968	YearBuilt_1969	YearBuilt_1970	YearBuilt_1971	YearBuilt_1972	YearBuilt_1973	YearBuilt_1974	YearBuilt_1975	YearBuilt_1976	YearBuilt_1977	YearBuilt_1978	YearBuilt_1979	YearBuilt_1980	YearBuilt_1981	YearBuilt_1982	YearBuilt_1983	YearBuilt_1984	YearBuilt_1985	YearBuilt_1986	YearBuilt_1987	YearBuilt_1988	YearBuilt_1989	YearBuilt_1990	YearBuilt_1991	YearBuilt_1992	YearBuilt_1993	YearBuilt_1994	YearBuilt_1995	YearBuilt_1996	YearBuilt_1997	YearBuilt_1998	YearBuilt_1999	YearBuilt_2000	YearBuilt_2001	YearBuilt_2002	YearBuilt_2003	YearBuilt_2004	YearBuilt_2005	YearBuilt_2006	YearBuilt_2007	YearBuilt_2008	YearBuilt_2009	YearBuilt_2010	YearRemodAdd_1950	YearRemodAdd_1951	YearRemodAdd_1952	YearRemodAdd_1953	YearRemodAdd_1954	YearRemodAdd_1955	YearRemodAdd_1956	YearRemodAdd_1957	YearRemodAdd_1958	YearRemodAdd_1959	YearRemodAdd_1960	YearRemodAdd_1961	YearRemodAdd_1962	YearRemodAdd_1963	YearRemodAdd_1964	YearRemodAdd_1965	YearRemodAdd_1966	YearRemodAdd_1967	YearRemodAdd_1968	YearRemodAdd_1969	YearRemodAdd_1970	YearRemodAdd_1971	YearRemodAdd_1972	YearRemodAdd_1973	YearRemodAdd_1974	YearRemodAdd_1975	YearRemodAdd_1976	YearRemodAdd_1977	YearRemodAdd_1978	YearRemodAdd_1979	YearRemodAdd_1980	YearRemodAdd_1981	YearRemodAdd_1982	YearRemodAdd_1983	YearRemodAdd_1984	YearRemodAdd_1985	YearRemodAdd_1986	YearRemodAdd_1987	YearRemodAdd_1988	YearRemodAdd_1989	YearRemodAdd_1990	YearRemodAdd_1991	YearRemodAdd_1992	YearRemodAdd_1993	YearRemodAdd_1994	YearRemodAdd_1995	YearRemodAdd_1996	YearRemodAdd_1997	YearRemodAdd_1998	YearRemodAdd_1999	YearRemodAdd_2000	YearRemodAdd_2001	YearRemodAdd_2002	YearRemodAdd_2003	YearRemodAdd_2004	YearRemodAdd_2005	YearRemodAdd_2006	YearRemodAdd_2007	YearRemodAdd_2008	YearRemodAdd_2009	YearRemodAdd_2010	RoofStyle_Flat	RoofStyle_Gable	RoofStyle_Gambrel	RoofStyle_Hip	RoofStyle_Mansard	RoofStyle_Shed	RoofMatl_ClyTile	RoofMatl_CompShg	RoofMatl_Membran	RoofMatl_Metal	RoofMatl_Roll	RoofMatl_Tar&Grv	RoofMatl_WdShake	RoofMatl_WdShngl	Exterior1st_AsbShng	Exterior1st_AsphShn	Exterior1st_BrkComm	Exterior1st_BrkFace	Exterior1st_CBlock	Exterior1st_CemntBd	Exterior1st_HdBoard	Exterior1st_ImStucc	Exterior1st_MetalSd	Exterior1st_Plywood	Exterior1st_Stone	Exterior1st_Stucco	Exterior1st_VinylSd	Exterior1st_Wd Sdng	Exterior1st_WdShing	Exterior2nd_AsbShng	Exterior2nd_AsphShn	Exterior2nd_Brk Cmn	Exterior2nd_BrkFace	Exterior2nd_CBlock	Exterior2nd_CmentBd	Exterior2nd_HdBoard	Exterior2nd_ImStucc	Exterior2nd_MetalSd	Exterior2nd_Other	Exterior2nd_Plywood	Exterior2nd_Stone	Exterior2nd_Stucco	Exterior2nd_VinylSd	Exterior2nd_Wd Sdng	Exterior2nd_Wd Shng	MasVnrType_BrkCmn	MasVnrType_BrkFace	MasVnrType_None	MasVnrType_Stone	ExterQual_Ex	ExterQual_Fa	ExterQual_Gd	ExterQual_TA	ExterCond_Ex	ExterCond_Fa	ExterCond_Gd	ExterCond_Po	ExterCond_TA	Foundation_BrkTil	Foundation_CBlock	Foundation_PConc	Foundation_Slab	Foundation_Stone	Foundation_Wood	BsmtQual_Ex	BsmtQual_Fa	BsmtQual_Gd	BsmtQual_None	BsmtQual_TA	BsmtCond_Fa	BsmtCond_Gd	BsmtCond_None	BsmtCond_Po	BsmtCond_TA	BsmtExposure_Av	BsmtExposure_Gd	BsmtExposure_Mn	BsmtExposure_No	BsmtExposure_None	BsmtFinType1_ALQ	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_LwQ	BsmtFinType1_None	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_ALQ	BsmtFinType2_BLQ	BsmtFinType2_GLQ	BsmtFinType2_LwQ	BsmtFinType2_None	BsmtFinType2_Rec	BsmtFinType2_Unf	Heating_Floor	Heating_GasA	Heating_GasW	Heating_Grav	Heating_OthW	Heating_Wall	HeatingQC_Ex	HeatingQC_Fa	HeatingQC_Gd	HeatingQC_Po	HeatingQC_TA	CentralAir_N	CentralAir_Y	Electrical_FuseA	Electrical_FuseF	Electrical_FuseP	Electrical_Mix	Electrical_SBrkr	FullBath_0	FullBath_1	FullBath_2	FullBath_3	FullBath_4	HalfBath_0	HalfBath_1	HalfBath_2	BedroomAbvGr_0	BedroomAbvGr_1	BedroomAbvGr_2	BedroomAbvGr_3	BedroomAbvGr_4	BedroomAbvGr_5	BedroomAbvGr_6	BedroomAbvGr_8	KitchenAbvGr_0	KitchenAbvGr_1	KitchenAbvGr_2	KitchenAbvGr_3	KitchenQual_Ex	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Maj1	Functional_Maj2	Functional_Min1	Functional_Min2	Functional_Mod	Functional_Sev	Functional_Typ	Fireplaces_0	Fireplaces_1	Fireplaces_2	Fireplaces_3	Fireplaces_4	FireplaceQu_Ex	FireplaceQu_Fa	FireplaceQu_Gd	FireplaceQu_None	FireplaceQu_Po	FireplaceQu_TA	GarageType_2Types	GarageType_Attchd	GarageType_Basment	GarageType_BuiltIn	GarageType_CarPort	GarageType_Detchd	GarageType_None	GarageFinish_Fin	GarageFinish_None	GarageFinish_RFn	GarageFinish_Unf	GarageCars_0.0	GarageCars_1.0	GarageCars_2.0	GarageCars_3.0	GarageCars_4.0	GarageCars_5.0	GarageCars_nan	GarageQual_Ex	GarageQual_Fa	GarageQual_Gd	GarageQual_None	GarageQual_Po	GarageQual_TA	GarageCond_Ex	GarageCond_Fa	GarageCond_Gd	GarageCond_None	GarageCond_Po	GarageCond_TA	PavedDrive_N	PavedDrive_P	PavedDrive_Y	PoolQC_Ex	PoolQC_Fa	PoolQC_Gd	PoolQC_None	Fence_GdPrv	Fence_GdWo	Fence_MnPrv	Fence_MnWw	Fence_None	MiscFeature_Gar2	MiscFeature_None	MiscFeature_Othr	MiscFeature_Shed	MiscFeature_TenC	YrSold_2006	YrSold_2007	YrSold_2008	YrSold_2009	YrSold_2010	SaleType_COD	SaleType_CWD	SaleType_Con	SaleType_ConLD	SaleType_ConLI	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	4.189655	9.042040	5.283204	6.561031	0.0	5.017280	6.753438	6.753438	6.751101	0.0	7.444833	0.693147	0.000000	2.197225	2003.0	548.0	0.000000	4.127134	0.000000	0.0	0.0	0.0	0.000000	-0.499998	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
1	4.394449	9.169623	0.000000	6.886532	0.0	5.652489	7.141245	7.141245	0.000000	0.0	7.141245	0.000000	0.693147	1.945910	1976.0	460.0	5.700444	0.000000	0.000000	0.0	0.0	0.0	0.000000	0.866028	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
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4	4.442651	9.565284	5.860786	6.486161	0.0	6.196444	7.044033	7.044033	6.960348	0.0	7.695758	0.693147	0.000000	2.302585	2000.0	836.0	5.262690	4.442651	0.000000	0.0	0.0	0.0	0.000000	-1.000000	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
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2917	4.143135	9.253591	0.000000	5.823046	0.0	6.356108	6.816736	6.878326	0.000000	0.0	6.878326	0.000000	0.693147	1.945910	1966.2	0.0	4.394449	3.496508	0.000000	0.0	0.0	0.0	6.552508	0.866021	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
2918	4.317488	9.172431	4.553877	6.632002	0.0	5.476464	6.904751	6.904751	6.912743	0.0	7.601402	0.000000	0.000000	2.302585	1993.0	650.0	5.252273	3.891820	0.000000	0.0	0.0	0.0	0.000000	-0.866032	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0

2919 rows × 541 columns

data7 = data6.copy()

Scaling

# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
scaler = StandardScaler()
scaler.fit(data7)

data6 = pd.DataFrame(scaler.transform(data7), index=data6.index, columns=data6.columns)

data7

	LotFrontage	LotArea	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	TotRmsAbvGrd	GarageYrBlt	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	MSSubClass_120	MSSubClass_150	MSSubClass_160	MSSubClass_180	MSSubClass_190	MSSubClass_20	MSSubClass_30	MSSubClass_40	MSSubClass_45	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSSubClass_75	MSSubClass_80	MSSubClass_85	MSSubClass_90	MSZoning_C (all)	MSZoning_FV	MSZoning_RH	MSZoning_RL	MSZoning_RM	Street_Grvl	Street_Pave	Alley_Grvl	Alley_None	Alley_Pave	LotShape_IR1	LotShape_IR2	LotShape_IR3	LotShape_Reg	LandContour_Bnk	LandContour_HLS	LandContour_Low	LandContour_Lvl	Utilities_AllPub	Utilities_NoSeWa	LotConfig_Corner	LotConfig_CulDSac	LotConfig_FR2	LotConfig_FR3	LotConfig_Inside	LandSlope_Gtl	LandSlope_Mod	LandSlope_Sev	Neighborhood_Blmngtn	Neighborhood_Blueste	Neighborhood_BrDale	Neighborhood_BrkSide	Neighborhood_ClearCr	Neighborhood_CollgCr	Neighborhood_Crawfor	Neighborhood_Edwards	Neighborhood_Gilbert	Neighborhood_IDOTRR	Neighborhood_MeadowV	Neighborhood_Mitchel	Neighborhood_NAmes	Neighborhood_NPkVill	Neighborhood_NWAmes	Neighborhood_NoRidge	Neighborhood_NridgHt	Neighborhood_OldTown	Neighborhood_SWISU	Neighborhood_Sawyer	Neighborhood_SawyerW	Neighborhood_Somerst	Neighborhood_StoneBr	Neighborhood_Timber	Neighborhood_Veenker	Condition1_Artery	Condition1_Feedr	Condition1_Norm	Condition1_PosA	Condition1_PosN	Condition1_RRAe	Condition1_RRAn	Condition1_RRNe	Condition1_RRNn	Condition2_Artery	Condition2_Feedr	Condition2_Norm	Condition2_PosA	Condition2_PosN	Condition2_RRAe	Condition2_RRAn	Condition2_RRNn	BldgType_1Fam	BldgType_2fmCon	BldgType_Duplex	BldgType_Twnhs	BldgType_TwnhsE	HouseStyle_1.5Fin	HouseStyle_1.5Unf	HouseStyle_1Story	HouseStyle_2.5Fin	HouseStyle_2.5Unf	HouseStyle_2Story	HouseStyle_SFoyer	HouseStyle_SLvl	OverallQual_1	OverallQual_10	OverallQual_2	OverallQual_3	OverallQual_4	OverallQual_5	OverallQual_6	OverallQual_7	OverallQual_8	OverallQual_9	OverallCond_1	OverallCond_2	OverallCond_3	OverallCond_4	OverallCond_5	OverallCond_6	OverallCond_7	OverallCond_8	OverallCond_9	YearBuilt_1872	YearBuilt_1875	YearBuilt_1879	YearBuilt_1880	YearBuilt_1882	YearBuilt_1885	YearBuilt_1890	YearBuilt_1892	YearBuilt_1893	YearBuilt_1895	YearBuilt_1896	YearBuilt_1898	YearBuilt_1900	YearBuilt_1901	YearBuilt_1902	YearBuilt_1904	YearBuilt_1905	YearBuilt_1906	YearBuilt_1907	YearBuilt_1908	YearBuilt_1910	YearBuilt_1911	YearBuilt_1912	YearBuilt_1913	YearBuilt_1914	YearBuilt_1915	YearBuilt_1916	YearBuilt_1917	YearBuilt_1918	YearBuilt_1919	YearBuilt_1920	YearBuilt_1921	YearBuilt_1922	YearBuilt_1923	YearBuilt_1924	YearBuilt_1925	YearBuilt_1926	YearBuilt_1927	YearBuilt_1928	YearBuilt_1929	YearBuilt_1930	YearBuilt_1931	YearBuilt_1932	YearBuilt_1934	YearBuilt_1935	YearBuilt_1936	YearBuilt_1937	YearBuilt_1938	YearBuilt_1939	YearBuilt_1940	YearBuilt_1941	YearBuilt_1942	YearBuilt_1945	YearBuilt_1946	YearBuilt_1947	YearBuilt_1948	YearBuilt_1949	YearBuilt_1950	YearBuilt_1951	YearBuilt_1952	YearBuilt_1953	YearBuilt_1954	YearBuilt_1955	YearBuilt_1956	YearBuilt_1957	YearBuilt_1958	YearBuilt_1959	YearBuilt_1960	YearBuilt_1961	YearBuilt_1962	YearBuilt_1963	YearBuilt_1964	YearBuilt_1965	YearBuilt_1966	YearBuilt_1967	YearBuilt_1968	YearBuilt_1969	YearBuilt_1970	YearBuilt_1971	YearBuilt_1972	YearBuilt_1973	YearBuilt_1974	YearBuilt_1975	YearBuilt_1976	YearBuilt_1977	YearBuilt_1978	YearBuilt_1979	YearBuilt_1980	YearBuilt_1981	YearBuilt_1982	YearBuilt_1983	YearBuilt_1984	YearBuilt_1985	YearBuilt_1986	YearBuilt_1987	YearBuilt_1988	YearBuilt_1989	YearBuilt_1990	YearBuilt_1991	YearBuilt_1992	YearBuilt_1993	YearBuilt_1994	YearBuilt_1995	YearBuilt_1996	YearBuilt_1997	YearBuilt_1998	YearBuilt_1999	YearBuilt_2000	YearBuilt_2001	YearBuilt_2002	YearBuilt_2003	YearBuilt_2004	YearBuilt_2005	YearBuilt_2006	YearBuilt_2007	YearBuilt_2008	YearBuilt_2009	YearBuilt_2010	YearRemodAdd_1950	YearRemodAdd_1951	YearRemodAdd_1952	YearRemodAdd_1953	YearRemodAdd_1954	YearRemodAdd_1955	YearRemodAdd_1956	YearRemodAdd_1957	YearRemodAdd_1958	YearRemodAdd_1959	YearRemodAdd_1960	YearRemodAdd_1961	YearRemodAdd_1962	YearRemodAdd_1963	YearRemodAdd_1964	YearRemodAdd_1965	YearRemodAdd_1966	YearRemodAdd_1967	YearRemodAdd_1968	YearRemodAdd_1969	YearRemodAdd_1970	YearRemodAdd_1971	YearRemodAdd_1972	YearRemodAdd_1973	YearRemodAdd_1974	YearRemodAdd_1975	YearRemodAdd_1976	YearRemodAdd_1977	YearRemodAdd_1978	YearRemodAdd_1979	YearRemodAdd_1980	YearRemodAdd_1981	YearRemodAdd_1982	YearRemodAdd_1983	YearRemodAdd_1984	YearRemodAdd_1985	YearRemodAdd_1986	YearRemodAdd_1987	YearRemodAdd_1988	YearRemodAdd_1989	YearRemodAdd_1990	YearRemodAdd_1991	YearRemodAdd_1992	YearRemodAdd_1993	YearRemodAdd_1994	YearRemodAdd_1995	YearRemodAdd_1996	YearRemodAdd_1997	YearRemodAdd_1998	YearRemodAdd_1999	YearRemodAdd_2000	YearRemodAdd_2001	YearRemodAdd_2002	YearRemodAdd_2003	YearRemodAdd_2004	YearRemodAdd_2005	YearRemodAdd_2006	YearRemodAdd_2007	YearRemodAdd_2008	YearRemodAdd_2009	YearRemodAdd_2010	RoofStyle_Flat	RoofStyle_Gable	RoofStyle_Gambrel	RoofStyle_Hip	RoofStyle_Mansard	RoofStyle_Shed	RoofMatl_ClyTile	RoofMatl_CompShg	RoofMatl_Membran	RoofMatl_Metal	RoofMatl_Roll	RoofMatl_Tar&Grv	RoofMatl_WdShake	RoofMatl_WdShngl	Exterior1st_AsbShng	Exterior1st_AsphShn	Exterior1st_BrkComm	Exterior1st_BrkFace	Exterior1st_CBlock	Exterior1st_CemntBd	Exterior1st_HdBoard	Exterior1st_ImStucc	Exterior1st_MetalSd	Exterior1st_Plywood	Exterior1st_Stone	Exterior1st_Stucco	Exterior1st_VinylSd	Exterior1st_Wd Sdng	Exterior1st_WdShing	Exterior2nd_AsbShng	Exterior2nd_AsphShn	Exterior2nd_Brk Cmn	Exterior2nd_BrkFace	Exterior2nd_CBlock	Exterior2nd_CmentBd	Exterior2nd_HdBoard	Exterior2nd_ImStucc	Exterior2nd_MetalSd	Exterior2nd_Other	Exterior2nd_Plywood	Exterior2nd_Stone	Exterior2nd_Stucco	Exterior2nd_VinylSd	Exterior2nd_Wd Sdng	Exterior2nd_Wd Shng	MasVnrType_BrkCmn	MasVnrType_BrkFace	MasVnrType_None	MasVnrType_Stone	ExterQual_Ex	ExterQual_Fa	ExterQual_Gd	ExterQual_TA	ExterCond_Ex	ExterCond_Fa	ExterCond_Gd	ExterCond_Po	ExterCond_TA	Foundation_BrkTil	Foundation_CBlock	Foundation_PConc	Foundation_Slab	Foundation_Stone	Foundation_Wood	BsmtQual_Ex	BsmtQual_Fa	BsmtQual_Gd	BsmtQual_None	BsmtQual_TA	BsmtCond_Fa	BsmtCond_Gd	BsmtCond_None	BsmtCond_Po	BsmtCond_TA	BsmtExposure_Av	BsmtExposure_Gd	BsmtExposure_Mn	BsmtExposure_No	BsmtExposure_None	BsmtFinType1_ALQ	BsmtFinType1_BLQ	BsmtFinType1_GLQ	BsmtFinType1_LwQ	BsmtFinType1_None	BsmtFinType1_Rec	BsmtFinType1_Unf	BsmtFinType2_ALQ	BsmtFinType2_BLQ	BsmtFinType2_GLQ	BsmtFinType2_LwQ	BsmtFinType2_None	BsmtFinType2_Rec	BsmtFinType2_Unf	Heating_Floor	Heating_GasA	Heating_GasW	Heating_Grav	Heating_OthW	Heating_Wall	HeatingQC_Ex	HeatingQC_Fa	HeatingQC_Gd	HeatingQC_Po	HeatingQC_TA	CentralAir_N	CentralAir_Y	Electrical_FuseA	Electrical_FuseF	Electrical_FuseP	Electrical_Mix	Electrical_SBrkr	FullBath_0	FullBath_1	FullBath_2	FullBath_3	FullBath_4	HalfBath_0	HalfBath_1	HalfBath_2	BedroomAbvGr_0	BedroomAbvGr_1	BedroomAbvGr_2	BedroomAbvGr_3	BedroomAbvGr_4	BedroomAbvGr_5	BedroomAbvGr_6	BedroomAbvGr_8	KitchenAbvGr_0	KitchenAbvGr_1	KitchenAbvGr_2	KitchenAbvGr_3	KitchenQual_Ex	KitchenQual_Fa	KitchenQual_Gd	KitchenQual_TA	Functional_Maj1	Functional_Maj2	Functional_Min1	Functional_Min2	Functional_Mod	Functional_Sev	Functional_Typ	Fireplaces_0	Fireplaces_1	Fireplaces_2	Fireplaces_3	Fireplaces_4	FireplaceQu_Ex	FireplaceQu_Fa	FireplaceQu_Gd	FireplaceQu_None	FireplaceQu_Po	FireplaceQu_TA	GarageType_2Types	GarageType_Attchd	GarageType_Basment	GarageType_BuiltIn	GarageType_CarPort	GarageType_Detchd	GarageType_None	GarageFinish_Fin	GarageFinish_None	GarageFinish_RFn	GarageFinish_Unf	GarageCars_0.0	GarageCars_1.0	GarageCars_2.0	GarageCars_3.0	GarageCars_4.0	GarageCars_5.0	GarageCars_nan	GarageQual_Ex	GarageQual_Fa	GarageQual_Gd	GarageQual_None	GarageQual_Po	GarageQual_TA	GarageCond_Ex	GarageCond_Fa	GarageCond_Gd	GarageCond_None	GarageCond_Po	GarageCond_TA	PavedDrive_N	PavedDrive_P	PavedDrive_Y	PoolQC_Ex	PoolQC_Fa	PoolQC_Gd	PoolQC_None	Fence_GdPrv	Fence_GdWo	Fence_MnPrv	Fence_MnWw	Fence_None	MiscFeature_Gar2	MiscFeature_None	MiscFeature_Othr	MiscFeature_Shed	MiscFeature_TenC	YrSold_2006	YrSold_2007	YrSold_2008	YrSold_2009	YrSold_2010	SaleType_COD	SaleType_CWD	SaleType_Con	SaleType_ConLD	SaleType_ConLI	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	4.189655	9.042040	5.283204	6.561031	0.0	5.017280	6.753438	6.753438	6.751101	0.0	7.444833	0.693147	0.000000	2.197225	2003.0	548.0	0.000000	4.127134	0.000000	0.0	0.0	0.0	0.000000	-0.499998	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
1	4.394449	9.169623	0.000000	6.886532	0.0	5.652489	7.141245	7.141245	0.000000	0.0	7.141245	0.000000	0.693147	1.945910	1976.0	460.0	5.700444	0.000000	0.000000	0.0	0.0	0.0	0.000000	0.866028	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
2	4.234107	9.328212	5.093750	6.188264	0.0	6.075346	6.825460	6.825460	6.765039	0.0	7.488294	0.693147	0.000000	1.945910	2001.0	608.0	0.000000	3.761200	0.000000	0.0	0.0	0.0	0.000000	-0.000011	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
3	4.110874	9.164401	0.000000	5.379897	0.0	6.293419	6.629363	6.869014	6.629363	0.0	7.448916	0.693147	0.000000	2.079442	1998.0	642.0	0.000000	3.583519	5.609472	0.0	0.0	0.0	0.000000	-0.499998	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
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2915	3.091042	7.546974	0.000000	5.533389	0.0	5.686975	6.304449	6.304449	6.304449	0.0	6.996681	0.000000	0.000000	1.945910	1970.0	286.0	0.000000	3.218876	0.000000	0.0	0.0	0.0	0.000000	0.500004	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
2916	5.081404	9.903538	0.000000	7.110696	0.0	0.000000	7.110696	7.110696	0.000000	0.0	7.110696	0.693147	0.000000	2.079442	1960.0	576.0	6.163315	0.000000	0.000000	0.0	0.0	0.0	0.000000	-0.000011	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
2917	4.143135	9.253591	0.000000	5.823046	0.0	6.356108	6.816736	6.878326	0.000000	0.0	6.878326	0.000000	0.693147	1.945910	1966.2	0.0	4.394449	3.496508	0.000000	0.0	0.0	0.0	6.552508	0.866021	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
2918	4.317488	9.172431	4.553877	6.632002	0.0	5.476464	6.904751	6.904751	6.912743	0.0	7.601402	0.000000	0.000000	2.302585	1993.0	650.0	5.252273	3.891820	0.000000	0.0	0.0	0.0	0.000000	-0.866032	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0

2919 rows × 541 columns

data8 = data7.copy()

Target Transformation

plt.figure(figsize=(20,10))

plt.subplot(1, 2, 1)
sns.distplot(target, kde=True, fit=scipy.stats.norm)
plt.title("Without log transform")

plt.subplot(1, 2, 2)
# wa7na mssh khyfeen mn al log 3shan 22l haga fal target greater than zero f msh htbooz
sns.distplot(np.log(target), kde=True, fit=scipy.stats.norm)
plt.xlabel('Log SalePrice')
plt.title("With log transform")

plt.show();

log_target = np.log(target)

log_target 
# lw 3ayz arg3 ll main target exp al target y3ny 3ks al log wkhlas afhm b2a
# np.exp(log_target)

0       12.247694
1       12.109011
2       12.317167
3       11.849398
4       12.429216
          ...    
1455    12.072541
1456    12.254863
1457    12.493130
1458    11.864462
1459    11.901583
Name: SalePrice, Length: 1460, dtype: float64

# print(train0.shape)
# print(test0.shape)
# print(data6.shape)

Split Data

print(data8.shape)
print(train0.shape)
print(test0.shape)

(2919, 541)
(1460, 81)
(1459, 80)

train_final = data7.loc[:train0.index.max(), :].copy()
test_final = data7.loc[train0.index.max()+1:, :].reset_index(drop=True).copy()

print(train_final.shape)
print(test_final.shape)

(1460, 541)
(1459, 541)

Model Selection

# _ = setup(data = pd.concat([train_final, log_target], axis=1), target='SalePrice')

	Description	Value
0	Session id	7468
1	Target	SalePrice
2	Target type	Regression
3	Original data shape	(1460, 542)
4	Transformed data shape	(1460, 542)
5	Transformed train set shape	(1021, 542)
6	Transformed test set shape	(439, 542)
7	Numeric features	541
8	Preprocess	True
9	Imputation type	simple
10	Numeric imputation	mean
11	Categorical imputation	mode
12	Fold Generator	KFold
13	Fold Number	10
14	CPU Jobs	-1
15	Use GPU	False
16	Log Experiment	False
17	Experiment Name	reg-default-name
18	USI	ca75

# https://towardsdatascience.com/5-things-you-are-doing-wrong-in-pycaret-e01981575d2a
# top10models = compare_models(n_select=10)
# doul a7sn 5 model momken astkhdmhom ll data bt3ty bs tb3n wla wa7d fehom drsnhom f hnshoof hn3ml eh

Initiated	. . . . . . . . . . . . . . . . . .	01:12:49
Status	. . . . . . . . . . . . . . . . . .	Loading Dependencies
Estimator	. . . . . . . . . . . . . . . . . .	Compiling Library

# predict_model(top5[0])
# top5
print("Best Model ",top10models[0])

Best Model  <catboost.core.CatBoostRegressor object at 0x000001AE3B5814B0>

Baseline Model

#Using CatBoost since that it had best results

model = CatBoostRegressor(random_state=42,verbose=0,iterations=300,depth=6,learning_rate=0.1,l2_leaf_reg=1)
model.fit(train_final,log_target)
final_predict = np.exp(model.predict(test_final))
# Using cat regressor gives us the best results

# baseline_model = CatBoostRegressor(verbose=0)

# baseline_model.fit(train_final, log_target)

<catboost.core.CatBoostRegressor at 0x1ae3ba09a20>

# https://scikit-learn.org/stable/modules/model_evaluation.html
# kf = KFold(n_splits=10)
# results = cross_val_score(baseline_model, train_final, log_target, scoring = 'neg_mean_squared_error', cv =kf)

# print(len(log_target))
# print(results)
# r2_score(log_target, results)
# -results

[-0.01248682 -0.00929631 -0.01126055 -0.02214197 -0.02001481 -0.00962841
 -0.01471818 -0.00903592 -0.01462041 -0.01563809]

# np.exp(np.sqrt(np.mean(-results)))

1.1250539770366386

# final_predict = baseline_model.predict(test_final)
# final_true = sample_submission['SalePrice']
# print(final_predict.shape)
# print(final_true.shape)
# final_predict = np.exp(final_predict)
# final_true = np.log1p(final_true)
# print(np.array(final_true))
# print(final_predict)
# final_predict

# print("r2 = ", r2_score(final_true,final_predict))
# print("mean square error= ", mean_squared_error(final_true,final_predict))
# print("mean absolute error= ",mean_absolute_error(final_true, final_predict))

# print(final_predict.shape)
# print(log_target.shape)
# """ 
# 0       169277.052498
# 1       187758.393989
# 2       183583.683570
# 3       179317.477511
# 4       150730.079977
#             ...      
# 1454    167081.220949
# 1455    164788.778231
# 1456    219222.423400
# 1457    184924.279659
# 1458    187741.866657
# """

# plt.figure(figsize=(5,2.5))

# sns.displot(-results, bins = 10, kde=True)
# plt.show()

Try linear Regression

• waktshfna alhwa haga zaee alzft fhn3mlo comment wnsebo wnrmeeh fe ai hta

# lr_model = LinearRegression()

# lr_model.fit(train_final, log_target)

# kf = KFold(n_splits=10)
# results = cross_val_score(lr_model, train_final, log_target, scoring = 'neg_mean_squared_error', cv=kf)

# np.exp(np.sqrt(np.mean(-results)))
# np.mean(results)

# plt.figure(figsize=(10,5))

# sns.displot(-results, bins = 10, kde=True)

Make Sumbission

# sample_submission

test_ids

0       1461
1       1462
2       1463
3       1464
4       1465
        ... 
1454    2915
1455    2916
1456    2917
1457    2918
1458    2919
Name: Id, Length: 1459, dtype: int64

# final_predict = baseline_model.predict(test_final)
# r2_score(log_target,final_predict)
# print(final_predict.shape)
# print(log_target.shape)

(1459,)
(1460,)

submission = pd.concat([test_ids.astype("Int32"), pd.Series(final_predict, name='SalePrice')], axis =1)
submission

	Id	SalePrice
0	1461	126915.405899
1	1462	162756.271598
2	1463	195110.529369
3	1464	202098.147983
4	1465	183570.649343
...	...	...
1454	2915	81548.277094
1455	2916	81224.552519
1456	2917	156920.500310
1457	2918	109983.489207
1458	2919	214292.241544

1459 rows × 2 columns

submission.dtypes

Id             Int32
SalePrice    float64
dtype: object

# submission['Id'] = test
# submission["Id"] = submission["Id"].astype(int)
submission.to_csv('./submission.csv', index=False, header=True)